diff --git a/docs/tutorials/Example_searches.ipynb b/docs/tutorials/Example_searches.ipynb
index d2f8d57..41d3a57 100644
--- a/docs/tutorials/Example_searches.ipynb
+++ b/docs/tutorials/Example_searches.ipynb
@@ -69,7 +69,8 @@
     {
      "data": {
       "text/html": [
-       "MASTSearch object containing 244 data products<div>\n",
+       "MASTSearch object containing 244 data products \n",
+       "<div>\n",
        "<style scoped>\n",
        "    .dataframe tbody tr th:only-of-type {\n",
        "        vertical-align: middle;\n",
@@ -213,7 +214,8 @@
        "</div>"
       ],
       "text/plain": [
-       "MASTSearch object containing 244 data products       target_name pipeline mission  exptime  distance  year  \\\n",
+       "MASTSearch object containing 244 data products \n",
+       "       target_name pipeline mission  exptime  distance  year  \\\n",
        "0        158324245     SPOC    TESS    120.0       0.0  2019   \n",
        "1        158324245     SPOC    TESS    120.0       0.0  2019   \n",
        "2        158324245     SPOC    TESS    120.0       0.0  2019   \n",
@@ -382,8 +384,6 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>obs_index</th>\n",
-       "      <th>prod_index</th>\n",
        "      <th>intentType</th>\n",
        "      <th>obs_collection_obs</th>\n",
        "      <th>provenance_name</th>\n",
@@ -392,6 +392,8 @@
        "      <th>filters_obs</th>\n",
        "      <th>wavelength_region</th>\n",
        "      <th>target_name</th>\n",
+       "      <th>target_classification</th>\n",
+       "      <th>obs_id</th>\n",
        "      <th>...</th>\n",
        "      <th>size</th>\n",
        "      <th>parent_obsid</th>\n",
@@ -408,8 +410,6 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>124</td>\n",
        "      <td>science</td>\n",
        "      <td>TESS</td>\n",
        "      <td>SPOC</td>\n",
@@ -418,6 +418,8 @@
        "      <td>TESS</td>\n",
        "      <td>Optical</td>\n",
        "      <td>158324245</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>tess2019198215352-s0014-0000000158324245-0150-s</td>\n",
        "      <td>...</td>\n",
        "      <td>16426251</td>\n",
        "      <td>27448285</td>\n",
@@ -432,8 +434,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0</td>\n",
-       "      <td>126</td>\n",
        "      <td>science</td>\n",
        "      <td>TESS</td>\n",
        "      <td>SPOC</td>\n",
@@ -442,6 +442,8 @@
        "      <td>TESS</td>\n",
        "      <td>Optical</td>\n",
        "      <td>158324245</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>tess2019198215352-s0014-0000000158324245-0150-s</td>\n",
        "      <td>...</td>\n",
        "      <td>16337553</td>\n",
        "      <td>27448285</td>\n",
@@ -456,8 +458,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0</td>\n",
-       "      <td>128</td>\n",
        "      <td>science</td>\n",
        "      <td>TESS</td>\n",
        "      <td>SPOC</td>\n",
@@ -466,6 +466,8 @@
        "      <td>TESS</td>\n",
        "      <td>Optical</td>\n",
        "      <td>158324245</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>tess2019198215352-s0014-0000000158324245-0150-s</td>\n",
        "      <td>...</td>\n",
        "      <td>5571090</td>\n",
        "      <td>27448285</td>\n",
@@ -480,8 +482,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0</td>\n",
-       "      <td>129</td>\n",
        "      <td>science</td>\n",
        "      <td>TESS</td>\n",
        "      <td>SPOC</td>\n",
@@ -490,6 +490,8 @@
        "      <td>TESS</td>\n",
        "      <td>Optical</td>\n",
        "      <td>158324245</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>tess2019198215352-s0014-0000000158324245-0150-s</td>\n",
        "      <td>...</td>\n",
        "      <td>5082604</td>\n",
        "      <td>27448285</td>\n",
@@ -504,8 +506,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0</td>\n",
-       "      <td>130</td>\n",
        "      <td>science</td>\n",
        "      <td>TESS</td>\n",
        "      <td>SPOC</td>\n",
@@ -514,6 +514,8 @@
        "      <td>TESS</td>\n",
        "      <td>Optical</td>\n",
        "      <td>158324245</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>tess2019198215352-s0014-0000000158324245-0150-s</td>\n",
        "      <td>...</td>\n",
        "      <td>1322689</td>\n",
        "      <td>27448285</td>\n",
@@ -552,8 +554,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>239</th>\n",
-       "      <td>30</td>\n",
-       "      <td>119</td>\n",
        "      <td>science</td>\n",
        "      <td>Kepler</td>\n",
        "      <td>Kepler</td>\n",
@@ -562,6 +562,8 @@
        "      <td>KEPLER</td>\n",
        "      <td>OPTICAL</td>\n",
        "      <td>kplr009941662</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>kplr009941662_sc_Q003300033333333332</td>\n",
        "      <td>...</td>\n",
        "      <td>15570968</td>\n",
        "      <td>599738</td>\n",
@@ -576,8 +578,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>240</th>\n",
-       "      <td>30</td>\n",
-       "      <td>120</td>\n",
        "      <td>science</td>\n",
        "      <td>Kepler</td>\n",
        "      <td>Kepler</td>\n",
@@ -586,6 +586,8 @@
        "      <td>KEPLER</td>\n",
        "      <td>OPTICAL</td>\n",
        "      <td>kplr009941662</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>kplr009941662_sc_Q003300033333333332</td>\n",
        "      <td>...</td>\n",
        "      <td>107558363</td>\n",
        "      <td>599738</td>\n",
@@ -600,8 +602,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>241</th>\n",
-       "      <td>30</td>\n",
-       "      <td>121</td>\n",
        "      <td>science</td>\n",
        "      <td>Kepler</td>\n",
        "      <td>Kepler</td>\n",
@@ -610,6 +610,8 @@
        "      <td>KEPLER</td>\n",
        "      <td>OPTICAL</td>\n",
        "      <td>kplr009941662</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>kplr009941662_sc_Q003300033333333332</td>\n",
        "      <td>...</td>\n",
        "      <td>94369677</td>\n",
        "      <td>599738</td>\n",
@@ -624,8 +626,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>242</th>\n",
-       "      <td>30</td>\n",
-       "      <td>122</td>\n",
        "      <td>science</td>\n",
        "      <td>Kepler</td>\n",
        "      <td>Kepler</td>\n",
@@ -634,6 +634,8 @@
        "      <td>KEPLER</td>\n",
        "      <td>OPTICAL</td>\n",
        "      <td>kplr009941662</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>kplr009941662_sc_Q003300033333333332</td>\n",
        "      <td>...</td>\n",
        "      <td>92737715</td>\n",
        "      <td>599738</td>\n",
@@ -648,8 +650,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>243</th>\n",
-       "      <td>30</td>\n",
-       "      <td>123</td>\n",
        "      <td>science</td>\n",
        "      <td>Kepler</td>\n",
        "      <td>Kepler</td>\n",
@@ -658,6 +658,8 @@
        "      <td>KEPLER</td>\n",
        "      <td>OPTICAL</td>\n",
        "      <td>kplr009941662</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>kplr009941662_sc_Q003300033333333332</td>\n",
        "      <td>...</td>\n",
        "      <td>17482211</td>\n",
        "      <td>599738</td>\n",
@@ -672,63 +674,76 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>244 rows × 61 columns</p>\n",
+       "<p>244 rows × 59 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "     obs_index  prod_index intentType obs_collection_obs provenance_name  \\\n",
-       "0            0         124    science               TESS            SPOC   \n",
-       "1            0         126    science               TESS            SPOC   \n",
-       "2            0         128    science               TESS            SPOC   \n",
-       "3            0         129    science               TESS            SPOC   \n",
-       "4            0         130    science               TESS            SPOC   \n",
-       "..         ...         ...        ...                ...             ...   \n",
-       "239         30         119    science             Kepler          Kepler   \n",
-       "240         30         120    science             Kepler          Kepler   \n",
-       "241         30         121    science             Kepler          Kepler   \n",
-       "242         30         122    science             Kepler          Kepler   \n",
-       "243         30         123    science             Kepler          Kepler   \n",
+       "    intentType obs_collection_obs provenance_name instrument_name project_obs  \\\n",
+       "0      science               TESS            SPOC      Photometer        TESS   \n",
+       "1      science               TESS            SPOC      Photometer        TESS   \n",
+       "2      science               TESS            SPOC      Photometer        TESS   \n",
+       "3      science               TESS            SPOC      Photometer        TESS   \n",
+       "4      science               TESS            SPOC      Photometer        TESS   \n",
+       "..         ...                ...             ...             ...         ...   \n",
+       "239    science             Kepler          Kepler          Kepler      Kepler   \n",
+       "240    science             Kepler          Kepler          Kepler      Kepler   \n",
+       "241    science             Kepler          Kepler          Kepler      Kepler   \n",
+       "242    science             Kepler          Kepler          Kepler      Kepler   \n",
+       "243    science             Kepler          Kepler          Kepler      Kepler   \n",
        "\n",
-       "    instrument_name project_obs filters_obs wavelength_region    target_name  \\\n",
-       "0        Photometer        TESS        TESS           Optical      158324245   \n",
-       "1        Photometer        TESS        TESS           Optical      158324245   \n",
-       "2        Photometer        TESS        TESS           Optical      158324245   \n",
-       "3        Photometer        TESS        TESS           Optical      158324245   \n",
-       "4        Photometer        TESS        TESS           Optical      158324245   \n",
-       "..              ...         ...         ...               ...            ...   \n",
-       "239          Kepler      Kepler      KEPLER           OPTICAL  kplr009941662   \n",
-       "240          Kepler      Kepler      KEPLER           OPTICAL  kplr009941662   \n",
-       "241          Kepler      Kepler      KEPLER           OPTICAL  kplr009941662   \n",
-       "242          Kepler      Kepler      KEPLER           OPTICAL  kplr009941662   \n",
-       "243          Kepler      Kepler      KEPLER           OPTICAL  kplr009941662   \n",
+       "    filters_obs wavelength_region    target_name target_classification  \\\n",
+       "0          TESS           Optical      158324245                   NaN   \n",
+       "1          TESS           Optical      158324245                   NaN   \n",
+       "2          TESS           Optical      158324245                   NaN   \n",
+       "3          TESS           Optical      158324245                   NaN   \n",
+       "4          TESS           Optical      158324245                   NaN   \n",
+       "..          ...               ...            ...                   ...   \n",
+       "239      KEPLER           OPTICAL  kplr009941662                   NaN   \n",
+       "240      KEPLER           OPTICAL  kplr009941662                   NaN   \n",
+       "241      KEPLER           OPTICAL  kplr009941662                   NaN   \n",
+       "242      KEPLER           OPTICAL  kplr009941662                   NaN   \n",
+       "243      KEPLER           OPTICAL  kplr009941662                   NaN   \n",
        "\n",
-       "     ...       size parent_obsid  dataRights  calib_level_prod filters_prod  \\\n",
-       "0    ...   16426251     27448285      PUBLIC                 3         TESS   \n",
-       "1    ...   16337553     27448285      PUBLIC                 3         TESS   \n",
-       "2    ...    5571090     27448285      PUBLIC                 3         TESS   \n",
-       "3    ...    5082604     27448285      PUBLIC                 3         TESS   \n",
-       "4    ...    1322689     27448285      PUBLIC                 3         TESS   \n",
-       "..   ...        ...          ...         ...               ...          ...   \n",
-       "239  ...   15570968       599738      PUBLIC                 2       KEPLER   \n",
-       "240  ...  107558363       599738      PUBLIC                 2       KEPLER   \n",
-       "241  ...   94369677       599738      PUBLIC                 2       KEPLER   \n",
-       "242  ...   92737715       599738      PUBLIC                 2       KEPLER   \n",
-       "243  ...   17482211       599738      PUBLIC                 2       KEPLER   \n",
+       "                                              obs_id  ...       size  \\\n",
+       "0    tess2019198215352-s0014-0000000158324245-0150-s  ...   16426251   \n",
+       "1    tess2019198215352-s0014-0000000158324245-0150-s  ...   16337553   \n",
+       "2    tess2019198215352-s0014-0000000158324245-0150-s  ...    5571090   \n",
+       "3    tess2019198215352-s0014-0000000158324245-0150-s  ...    5082604   \n",
+       "4    tess2019198215352-s0014-0000000158324245-0150-s  ...    1322689   \n",
+       "..                                               ...  ...        ...   \n",
+       "239             kplr009941662_sc_Q003300033333333332  ...   15570968   \n",
+       "240             kplr009941662_sc_Q003300033333333332  ...  107558363   \n",
+       "241             kplr009941662_sc_Q003300033333333332  ...   94369677   \n",
+       "242             kplr009941662_sc_Q003300033333333332  ...   92737715   \n",
+       "243             kplr009941662_sc_Q003300033333333332  ...   17482211   \n",
        "\n",
-       "    pipeline  mission  year               start_time                 end_time  \n",
-       "0       SPOC     TESS  2019  2019-07-18 20:30:33.624  2019-08-14 16:56:23.634  \n",
-       "1       SPOC     TESS  2019  2019-07-18 20:30:33.624  2019-08-14 16:56:23.634  \n",
-       "2       SPOC     TESS  2019  2019-07-18 20:30:33.624  2019-08-14 16:56:23.634  \n",
-       "3       SPOC     TESS  2019  2019-07-18 20:30:33.624  2019-08-14 16:56:23.634  \n",
-       "4       SPOC     TESS  2019  2019-07-18 20:30:33.624  2019-08-14 16:56:23.634  \n",
-       "..       ...      ...   ...                      ...                      ...  \n",
-       "239   Kepler   Kepler  2013  2009-05-02 00:54:00.000  2013-05-11 12:15:00.000  \n",
-       "240   Kepler   Kepler  2013  2009-05-02 00:54:00.000  2013-05-11 12:15:00.000  \n",
-       "241   Kepler   Kepler  2013  2009-05-02 00:54:00.000  2013-05-11 12:15:00.000  \n",
-       "242   Kepler   Kepler  2013  2009-05-02 00:54:00.000  2013-05-11 12:15:00.000  \n",
-       "243   Kepler   Kepler  2013  2009-05-02 00:54:00.000  2013-05-11 12:15:00.000  \n",
+       "     parent_obsid dataRights calib_level_prod  filters_prod  pipeline  \\\n",
+       "0        27448285     PUBLIC                3          TESS      SPOC   \n",
+       "1        27448285     PUBLIC                3          TESS      SPOC   \n",
+       "2        27448285     PUBLIC                3          TESS      SPOC   \n",
+       "3        27448285     PUBLIC                3          TESS      SPOC   \n",
+       "4        27448285     PUBLIC                3          TESS      SPOC   \n",
+       "..            ...        ...              ...           ...       ...   \n",
+       "239        599738     PUBLIC                2        KEPLER    Kepler   \n",
+       "240        599738     PUBLIC                2        KEPLER    Kepler   \n",
+       "241        599738     PUBLIC                2        KEPLER    Kepler   \n",
+       "242        599738     PUBLIC                2        KEPLER    Kepler   \n",
+       "243        599738     PUBLIC                2        KEPLER    Kepler   \n",
        "\n",
-       "[244 rows x 61 columns]"
+       "     mission  year               start_time                 end_time  \n",
+       "0       TESS  2019  2019-07-18 20:30:33.624  2019-08-14 16:56:23.634  \n",
+       "1       TESS  2019  2019-07-18 20:30:33.624  2019-08-14 16:56:23.634  \n",
+       "2       TESS  2019  2019-07-18 20:30:33.624  2019-08-14 16:56:23.634  \n",
+       "3       TESS  2019  2019-07-18 20:30:33.624  2019-08-14 16:56:23.634  \n",
+       "4       TESS  2019  2019-07-18 20:30:33.624  2019-08-14 16:56:23.634  \n",
+       "..       ...   ...                      ...                      ...  \n",
+       "239   Kepler  2013  2009-05-02 00:54:00.000  2013-05-11 12:15:00.000  \n",
+       "240   Kepler  2013  2009-05-02 00:54:00.000  2013-05-11 12:15:00.000  \n",
+       "241   Kepler  2013  2009-05-02 00:54:00.000  2013-05-11 12:15:00.000  \n",
+       "242   Kepler  2013  2009-05-02 00:54:00.000  2013-05-11 12:15:00.000  \n",
+       "243   Kepler  2013  2009-05-02 00:54:00.000  2013-05-11 12:15:00.000  \n",
+       "\n",
+       "[244 rows x 59 columns]"
       ]
      },
      "execution_count": 5,
@@ -766,7 +781,8 @@
     {
      "data": {
       "text/html": [
-       "TESSSearch object containing 233 data products<div>\n",
+       "TESSSearch object containing 236 data products \n",
+       "<div>\n",
        "<style scoped>\n",
        "    .dataframe tbody tr th:only-of-type {\n",
        "        vertical-align: middle;\n",
@@ -862,78 +878,79 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>228</th>\n",
-       "      <td>1717079066</td>\n",
-       "      <td>CDIPS</td>\n",
+       "      <th>231</th>\n",
+       "      <td>1717079071</td>\n",
+       "      <td>QLP</td>\n",
        "      <td>HLSP</td>\n",
        "      <td>55</td>\n",
-       "      <td>1800.0</td>\n",
+       "      <td>600.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>2022</td>\n",
        "      <td>FITS</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>229</th>\n",
-       "      <td>158324245</td>\n",
-       "      <td>CDIPS</td>\n",
+       "      <th>232</th>\n",
+       "      <td>1717079066</td>\n",
+       "      <td>QLP</td>\n",
        "      <td>HLSP</td>\n",
        "      <td>55</td>\n",
-       "      <td>1800.0</td>\n",
+       "      <td>600.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>2022</td>\n",
        "      <td>FITS</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>230</th>\n",
+       "      <th>233</th>\n",
        "      <td>158324245</td>\n",
        "      <td>QLP</td>\n",
        "      <td>HLSP</td>\n",
-       "      <td>55</td>\n",
-       "      <td>600.0</td>\n",
+       "      <td>74</td>\n",
+       "      <td>200.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2022</td>\n",
+       "      <td>2024</td>\n",
        "      <td>FITS</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>231</th>\n",
+       "      <th>234</th>\n",
        "      <td>1717079066</td>\n",
        "      <td>QLP</td>\n",
        "      <td>HLSP</td>\n",
-       "      <td>55</td>\n",
-       "      <td>600.0</td>\n",
+       "      <td>74</td>\n",
+       "      <td>200.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2022</td>\n",
+       "      <td>2024</td>\n",
        "      <td>FITS</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>232</th>\n",
+       "      <th>235</th>\n",
        "      <td>1717079071</td>\n",
        "      <td>QLP</td>\n",
        "      <td>HLSP</td>\n",
-       "      <td>55</td>\n",
-       "      <td>600.0</td>\n",
+       "      <td>74</td>\n",
+       "      <td>200.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2022</td>\n",
+       "      <td>2024</td>\n",
        "      <td>FITS</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>233 rows × 8 columns</p>\n",
+       "<p>236 rows × 8 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "TESSSearch object containing 233 data products    target_name pipeline mission  sector  exptime  distance  year  \\\n",
+       "TESSSearch object containing 236 data products \n",
+       "    target_name pipeline mission  sector  exptime  distance  year  \\\n",
        "0     158324245     SPOC    TESS      14    120.0       0.0  2019   \n",
        "1     158324245     SPOC    TESS      14    120.0       0.0  2019   \n",
        "2     158324245     SPOC    TESS      14    120.0       0.0  2019   \n",
        "3     158324245     SPOC    TESS      14    120.0       0.0  2019   \n",
        "4     158324245     SPOC    TESS      14    120.0       0.0  2019   \n",
        "..          ...      ...     ...     ...      ...       ...   ...   \n",
-       "228  1717079066    CDIPS    HLSP      55   1800.0       0.0  2022   \n",
-       "229   158324245    CDIPS    HLSP      55   1800.0       0.0  2022   \n",
-       "230   158324245      QLP    HLSP      55    600.0       0.0  2022   \n",
-       "231  1717079066      QLP    HLSP      55    600.0       0.0  2022   \n",
-       "232  1717079071      QLP    HLSP      55    600.0       0.0  2022   \n",
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        "\n",
        "                     description  \n",
        "0    full data validation report  \n",
@@ -942,13 +959,13 @@
        "3    Data validation mini report  \n",
        "4             TCE summary report  \n",
        "..                           ...  \n",
-       "228                         FITS  \n",
-       "229                         FITS  \n",
-       "230                         FITS  \n",
        "231                         FITS  \n",
        "232                         FITS  \n",
+       "233                         FITS  \n",
+       "234                         FITS  \n",
+       "235                         FITS  \n",
        "\n",
-       "[233 rows x 8 columns]"
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      },
      "execution_count": 6,
@@ -981,7 +998,8 @@
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        "\n",
-       "[83 rows x 8 columns]"
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      "execution_count": 7,
@@ -1173,7 +1192,8 @@
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        "0     158324245       SPOC    TESS      14    120.0       0.0  2019   \n",
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        "  </tbody>\n",
        "</table>\n",
-       "<p>105 rows × 8 columns</p>\n",
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        "</div>"
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-       "TESSSearch object containing 105 data products    target_name   pipeline mission  sector  exptime  distance  year  \\\n",
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        "0     158324245  TESS-SPOC    HLSP      14   1800.0       0.0  2019   \n",
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        "..          ...        ...     ...     ...      ...       ...   ...   \n",
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        "\n",
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@@ -1760,13 +1785,13 @@
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-       "102        FITS  \n",
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        "104        FITS  \n",
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        "\n",
-       "[105 rows x 8 columns]"
+       "[108 rows x 8 columns]"
       ]
      },
      "execution_count": 10,
@@ -1789,7 +1814,8 @@
     {
      "data": {
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-       "TESSSearch object containing 128 data products    target_name pipeline mission  sector  exptime  distance  year  \\\n",
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        "0     158324245     SPOC    TESS      14    120.0       0.0  2019   \n",
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@@ -2007,7 +2034,8 @@
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-       "TESSSearch object containing 8 data products  target_name pipeline mission  sector  exptime  distance  year  \\\n",
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        "0   158324245     SPOC    TESS      74     20.0       0.0  2024   \n",
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@@ -2171,7 +2200,8 @@
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-       "TESSSearch object containing 125 data products<div>\n",
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-       "      <th>123</th>\n",
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-       "      <td>2019</td>\n",
+       "      <td>2020</td>\n",
        "      <td>FITS</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>124</th>\n",
+       "      <th>125</th>\n",
        "      <td>158324245</td>\n",
-       "      <td>TASOC</td>\n",
+       "      <td>QLP</td>\n",
        "      <td>HLSP</td>\n",
-       "      <td>26</td>\n",
-       "      <td>120.0</td>\n",
+       "      <td>74</td>\n",
+       "      <td>200.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2020</td>\n",
+       "      <td>2024</td>\n",
+       "      <td>FITS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>126</th>\n",
+       "      <td>1717079066</td>\n",
+       "      <td>QLP</td>\n",
+       "      <td>HLSP</td>\n",
+       "      <td>74</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2024</td>\n",
+       "      <td>FITS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>1717079071</td>\n",
+       "      <td>QLP</td>\n",
+       "      <td>HLSP</td>\n",
+       "      <td>74</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2024</td>\n",
        "      <td>FITS</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>125 rows × 8 columns</p>\n",
+       "<p>128 rows × 8 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "TESSSearch object containing 125 data products    target_name pipeline         mission  sector  exptime  distance  year  \\\n",
-       "0     158324245     SPOC            TESS      14    120.0       0.0  2019   \n",
-       "1     158324245     SPOC            TESS      14    120.0       0.0  2019   \n",
-       "2     158324245     SPOC            TESS      14    120.0       0.0  2019   \n",
-       "3     158324245     SPOC            TESS      14    120.0       0.0  2019   \n",
-       "4     158324245     SPOC            TESS      14    120.0       0.0  2019   \n",
-       "..          ...      ...             ...     ...      ...       ...   ...   \n",
-       "120    TOI 1161  TESScut  TESS Sector 74      74    200.0       0.0  2024   \n",
-       "121    TOI 1161  TESScut  TESS Sector 75      75    200.0       0.0  2024   \n",
-       "122   158324245    TASOC            HLSP      14    120.0       0.0  2019   \n",
-       "123   158324245    TASOC            HLSP      15    120.0       0.0  2019   \n",
-       "124   158324245    TASOC            HLSP      26    120.0       0.0  2020   \n",
+       "TESSSearch object containing 128 data products \n",
+       "    target_name pipeline mission  sector  exptime  distance  year  \\\n",
+       "0     158324245     SPOC    TESS      14    120.0       0.0  2019   \n",
+       "1     158324245     SPOC    TESS      14    120.0       0.0  2019   \n",
+       "2     158324245     SPOC    TESS      14    120.0       0.0  2019   \n",
+       "3     158324245     SPOC    TESS      14    120.0       0.0  2019   \n",
+       "4     158324245     SPOC    TESS      14    120.0       0.0  2019   \n",
+       "..          ...      ...     ...     ...      ...       ...   ...   \n",
+       "123   158324245    TASOC    HLSP      15    120.0       0.0  2019   \n",
+       "124   158324245    TASOC    HLSP      26    120.0       0.0  2020   \n",
+       "125   158324245      QLP    HLSP      74    200.0       0.0  2024   \n",
+       "126  1717079066      QLP    HLSP      74    200.0       0.0  2024   \n",
+       "127  1717079071      QLP    HLSP      74    200.0       0.0  2024   \n",
        "\n",
        "                     description  \n",
        "0    full data validation report  \n",
@@ -2347,13 +2378,13 @@
        "3    Data validation mini report  \n",
        "4             TCE summary report  \n",
        "..                           ...  \n",
-       "120  TESS FFI Cutout (sector 74)  \n",
-       "121  TESS FFI Cutout (sector 75)  \n",
-       "122                         FITS  \n",
        "123                         FITS  \n",
        "124                         FITS  \n",
+       "125                         FITS  \n",
+       "126                         FITS  \n",
+       "127                         FITS  \n",
        "\n",
-       "[125 rows x 8 columns]"
+       "[128 rows x 8 columns]"
       ]
      },
      "execution_count": 13,
@@ -2376,7 +2407,8 @@
     {
      "data": {
       "text/html": [
-       "TESSSearch object containing 2 data products<div>\n",
+       "TESSSearch object containing 2 data products \n",
+       "<div>\n",
        "<style scoped>\n",
        "    .dataframe tbody tr th:only-of-type {\n",
        "        vertical-align: middle;\n",
@@ -2432,7 +2464,8 @@
        "</div>"
       ],
       "text/plain": [
-       "TESSSearch object containing 2 data products  target_name pipeline mission  sector  exptime  distance  year  \\\n",
+       "TESSSearch object containing 2 data products \n",
+       "  target_name pipeline mission  sector  exptime  distance  year  \\\n",
        "0   158324245     SPOC    TESS      14    120.0       0.0  2019   \n",
        "1   158324245     SPOC    TESS      14    120.0       0.0  2019   \n",
        "\n",
@@ -2468,7 +2501,8 @@
     {
      "data": {
       "text/html": [
-       "TESSSearch object containing 2 data products<div>\n",
+       "TESSSearch object containing 2 data products \n",
+       "<div>\n",
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@@ -2524,7 +2558,8 @@
        "</div>"
       ],
       "text/plain": [
-       "TESSSearch object containing 2 data products  target_name pipeline mission  sector  exptime  distance  year   description\n",
+       "TESSSearch object containing 2 data products \n",
+       "  target_name pipeline mission  sector  exptime  distance  year   description\n",
        "0   158324245     SPOC    TESS      14    120.0       0.0  2019  Light curves\n",
        "1   158324245    TASOC    HLSP      14    120.0       0.0  2019          FITS"
       ]
@@ -2557,21 +2592,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "pipeline products: 100%|██████████████████████████| 2/2 [00:03<00:00,  1.87s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "True\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
+      "pipeline products: 100%|████████████████████████████████████████████| 2/2 [00:00<00:00,  4.35it/s]\n"
      ]
     },
     {
@@ -2595,7 +2616,6 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>index</th>\n",
        "      <th>Local Path</th>\n",
        "      <th>Status</th>\n",
        "      <th>Message</th>\n",
@@ -2605,7 +2625,6 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0</td>\n",
        "      <td>/Users/tapritc2/.lksearch/cache/mastDownload/T...</td>\n",
        "      <td>COMPLETE</td>\n",
        "      <td>None</td>\n",
@@ -2613,7 +2632,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0</td>\n",
        "      <td>/Users/tapritc2/.lksearch/cache/mastDownload/H...</td>\n",
        "      <td>COMPLETE</td>\n",
        "      <td>None</td>\n",
@@ -2624,13 +2642,9 @@
        "</div>"
       ],
       "text/plain": [
-       "   index                                         Local Path    Status Message  \\\n",
-       "0      0  /Users/tapritc2/.lksearch/cache/mastDownload/T...  COMPLETE    None   \n",
-       "1      0  /Users/tapritc2/.lksearch/cache/mastDownload/H...  COMPLETE    None   \n",
-       "\n",
-       "    URL  \n",
-       "0  None  \n",
-       "1  None  "
+       "                                          Local Path    Status Message   URL\n",
+       "0  /Users/tapritc2/.lksearch/cache/mastDownload/T...  COMPLETE    None  None\n",
+       "1  /Users/tapritc2/.lksearch/cache/mastDownload/H...  COMPLETE    None  None"
       ]
      },
      "execution_count": 16,
@@ -2642,6 +2656,96 @@
     "toi_short_lcs.download()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "32fa37cf-09c3-4c90-b0ad-bef4ae0a9b54",
+   "metadata": {},
+   "source": [
+    "You can also query the pandas Dataframe back end to perform more complicated searches.  For example, the above could also be perfomed via:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "5173e574-4a1f-4fd7-9071-52fae2c86a24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "TESSSearch object containing 2 data products \n",
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>target_name</th>\n",
+       "      <th>pipeline</th>\n",
+       "      <th>mission</th>\n",
+       "      <th>sector</th>\n",
+       "      <th>exptime</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>year</th>\n",
+       "      <th>description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>158324245</td>\n",
+       "      <td>SPOC</td>\n",
+       "      <td>TESS</td>\n",
+       "      <td>14</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>Light curves</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>158324245</td>\n",
+       "      <td>TASOC</td>\n",
+       "      <td>HLSP</td>\n",
+       "      <td>14</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>FITS</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "TESSSearch object containing 2 data products \n",
+       "  target_name pipeline mission  sector  exptime  distance  year   description\n",
+       "0   158324245     SPOC    TESS      14    120.0       0.0  2019  Light curves\n",
+       "1   158324245    TASOC    HLSP      14    120.0       0.0  2019          FITS"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "toi_short_lcs = toi.timeseries.query_table('sector == 14 & exptime == 120')\n",
+    "toi_short_lcs"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "b97fca26",
@@ -2664,14 +2768,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "d2cfa795",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "TESSSearch object containing 6 data products<div>\n",
+       "TESSSearch object containing 6 data products \n",
+       "<div>\n",
        "<style scoped>\n",
        "    .dataframe tbody tr th:only-of-type {\n",
        "        vertical-align: middle;\n",
@@ -2771,7 +2876,8 @@
        "</div>"
       ],
       "text/plain": [
-       "TESSSearch object containing 6 data products  target_name pipeline         mission  sector  exptime  distance  year  \\\n",
+       "TESSSearch object containing 6 data products \n",
+       "  target_name pipeline         mission  sector  exptime  distance  year  \\\n",
        "0     TOI 270  TESScut  TESS Sector 03       3   1800.0       0.0  2018   \n",
        "1     TOI 270  TESScut  TESS Sector 04       4   1800.0       0.0  2018   \n",
        "2     TOI 270  TESScut  TESS Sector 05       5   1800.0       0.0  2018   \n",
@@ -2788,7 +2894,7 @@
        "5  TESS FFI Cutout (sector 32)  "
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2811,7 +2917,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "id": "0988e2c9",
    "metadata": {},
    "outputs": [
@@ -2819,7 +2925,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "TESScut          : 100%|██████████████████████████| 1/1 [00:03<00:00,  3.22s/it]\n"
+      "TESScut          : 100%|████████████████████████████████████████████| 1/1 [00:02<00:00,  2.88s/it]\n"
      ]
     },
     {
@@ -2862,7 +2968,7 @@
        "0  /Users/tapritc2/.lksearch/cache/mastDownload/T...  COMPLETE"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2885,14 +2991,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "id": "d7bac166-9110-4ed2-8341-0ced027b9b16",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "TESSSearch object containing 160 data products<div>\n",
+       "TESSSearch object containing 162 data products \n",
+       "<div>\n",
        "<style scoped>\n",
        "    .dataframe tbody tr th:only-of-type {\n",
        "        vertical-align: middle;\n",
@@ -2988,78 +3095,79 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>155</th>\n",
+       "      <th>157</th>\n",
        "      <td>299096355</td>\n",
        "      <td>CDIPS</td>\n",
        "      <td>HLSP</td>\n",
-       "      <td>54</td>\n",
+       "      <td>55</td>\n",
        "      <td>1800.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>2022</td>\n",
        "      <td>FITS</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>156</th>\n",
+       "      <th>158</th>\n",
        "      <td>299096355</td>\n",
        "      <td>QLP</td>\n",
        "      <td>HLSP</td>\n",
-       "      <td>54</td>\n",
+       "      <td>55</td>\n",
        "      <td>600.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>2022</td>\n",
        "      <td>FITS</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>157</th>\n",
+       "      <th>159</th>\n",
        "      <td>299096355</td>\n",
-       "      <td>CDIPS</td>\n",
+       "      <td>QLP</td>\n",
        "      <td>HLSP</td>\n",
-       "      <td>55</td>\n",
-       "      <td>1800.0</td>\n",
+       "      <td>56</td>\n",
+       "      <td>200.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>2022</td>\n",
        "      <td>FITS</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>158</th>\n",
+       "      <th>160</th>\n",
        "      <td>299096355</td>\n",
        "      <td>QLP</td>\n",
        "      <td>HLSP</td>\n",
-       "      <td>55</td>\n",
-       "      <td>600.0</td>\n",
+       "      <td>73</td>\n",
+       "      <td>200.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2022</td>\n",
+       "      <td>2023</td>\n",
        "      <td>FITS</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>159</th>\n",
+       "      <th>161</th>\n",
        "      <td>299096355</td>\n",
        "      <td>QLP</td>\n",
        "      <td>HLSP</td>\n",
-       "      <td>56</td>\n",
+       "      <td>74</td>\n",
        "      <td>200.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2022</td>\n",
+       "      <td>2024</td>\n",
        "      <td>FITS</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>160 rows × 8 columns</p>\n",
+       "<p>162 rows × 8 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "TESSSearch object containing 160 data products    target_name pipeline mission  sector  exptime  distance  year  \\\n",
+       "TESSSearch object containing 162 data products \n",
+       "    target_name pipeline mission  sector  exptime  distance  year  \\\n",
        "0     299096355     SPOC    TESS      14    120.0       0.0  2019   \n",
        "1     299096355     SPOC    TESS      14    120.0       0.0  2019   \n",
        "2     299096355     SPOC    TESS      14    120.0       0.0  2019   \n",
        "3     299096355     SPOC    TESS      14    120.0       0.0  2019   \n",
        "4     299096355     SPOC    TESS      14    120.0       0.0  2019   \n",
        "..          ...      ...     ...     ...      ...       ...   ...   \n",
-       "155   299096355    CDIPS    HLSP      54   1800.0       0.0  2022   \n",
-       "156   299096355      QLP    HLSP      54    600.0       0.0  2022   \n",
        "157   299096355    CDIPS    HLSP      55   1800.0       0.0  2022   \n",
        "158   299096355      QLP    HLSP      55    600.0       0.0  2022   \n",
        "159   299096355      QLP    HLSP      56    200.0       0.0  2022   \n",
+       "160   299096355      QLP    HLSP      73    200.0       0.0  2023   \n",
+       "161   299096355      QLP    HLSP      74    200.0       0.0  2024   \n",
        "\n",
        "                     description  \n",
        "0    full data validation report  \n",
@@ -3068,16 +3176,16 @@
        "3    Data validation mini report  \n",
        "4             TCE summary report  \n",
        "..                           ...  \n",
-       "155                         FITS  \n",
-       "156                         FITS  \n",
        "157                         FITS  \n",
        "158                         FITS  \n",
        "159                         FITS  \n",
+       "160                         FITS  \n",
+       "161                         FITS  \n",
        "\n",
-       "[160 rows x 8 columns]"
+       "[162 rows x 8 columns]"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3089,14 +3197,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "id": "a41178f6-2d5b-4e7c-808e-21d19eb7ff79",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "TESSSearch object containing 1241 data products<div>\n",
+       "TESSSearch object containing 1241 data products \n",
+       "<div>\n",
        "<style scoped>\n",
        "    .dataframe tbody tr th:only-of-type {\n",
        "        vertical-align: middle;\n",
@@ -3252,7 +3361,8 @@
        "</div>"
       ],
       "text/plain": [
-       "TESSSearch object containing 1241 data products                             target_name pipeline mission  sector  exptime  \\\n",
+       "TESSSearch object containing 1241 data products \n",
+       "                             target_name pipeline mission  sector  exptime  \\\n",
        "0     tess2019220045929-s0014-2-4-0150-s     SPOC    TESS      14     1800   \n",
        "1     tess2019200195929-s0014-2-4-0150-s     SPOC    TESS      14     1800   \n",
        "2     tess2019219202929-s0014-2-4-0150-s     SPOC    TESS      14     1800   \n",
@@ -3281,14 +3391,14 @@
        "[1241 rows x 8 columns]"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# tstart, tstop in mjd\n",
-    "ffis = search_result.search_individual_ffi(58682,58710, sector=14)\n",
+    "# Search for a list of FFIs in Sector 14\n",
+    "ffis = search_result.search_sector_ffis(14)\n",
     "ffis"
    ]
   },
@@ -3302,7 +3412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "id": "a84e244e",
    "metadata": {},
    "outputs": [
@@ -3310,21 +3420,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
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-       "   index                                         Local Path    Status Message  \\\n",
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-       "\n",
-       "    URL  \n",
-       "0  None  "
+       "                                          Local Path    Status Message   URL\n",
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@@ -3403,14 +3494,15 @@
   },
   {
    "cell_type": "code",
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    "id": "ace8aad4",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "KeplerSearch object containing 82 data products<div>\n",
+       "KeplerSearch object containing 82 data products \n",
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        "      <td>2013</td>\n",
-       "      <td>Target Pixel Long Cadence (TPL) - Q17</td>\n",
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-       "KeplerSearch object containing 82 data products                     target_name    pipeline mission  quarter   exptime  \\\n",
+       "KeplerSearch object containing 82 data products \n",
+       "                     target_name    pipeline mission  quarter   exptime  \\\n",
        "0                  kplr007419318      Kepler  Kepler        0  1800.000   \n",
        "1                  kplr007419318      Kepler  Kepler        0  1800.000   \n",
        "2                  kplr007419318      Kepler  Kepler        1  1800.000   \n",
@@ -3579,23 +3672,23 @@
        "80                 kplr007419318      Kepler  Kepler       99  1800.000   \n",
        "81  Gaia DR3 2104847370214740352  KBONUS-BKG    HLSP       99  1765.464   \n",
        "\n",
-       "    distance  year                            description  \n",
-       "0        0.0  2009     Lightcurve Long Cadence (CLC) - Q0  \n",
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-       "..       ...   ...                                    ...  \n",
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-       "79       0.0  2009         Data Validation summary report  \n",
-       "80       0.0  2009         Data Validation summary report  \n",
-       "81       0.0  2009                                   FITS  \n",
+       "    distance  year                           description  \n",
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+       "4        0.0  2009    Lightcurve Long Cadence (CLC) - Q2  \n",
+       "..       ...   ...                                   ...  \n",
+       "77       0.0  2013   Lightcurve Long Cadence (CLC) - Q17  \n",
+       "78       0.0  2009           Data Validation full report  \n",
+       "79       0.0  2009        Data Validation summary report  \n",
+       "80       0.0  2009        Data Validation summary report  \n",
+       "81       0.0  2009                                  FITS  \n",
        "\n",
        "[82 rows x 8 columns]"
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@@ -3608,14 +3701,15 @@
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    "outputs": [
     {
      "data": {
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-       "KeplerSearch object containing 40 data products<div>\n",
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-       "KeplerSearch object containing 40 data products                     target_name    pipeline mission  quarter   exptime  \\\n",
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        "0                  kplr007419318      Kepler  Kepler        0  1800.000   \n",
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-       "\n",
-       "    URL  \n",
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    "id": "0890bbc6",
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    "id": "502fee80",
    "metadata": {},
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     {
      "data": {
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-       "KeplerSearch object containing 10 data products<div>\n",
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-       "KeplerSearch object containing 10 data products     target_name pipeline mission  quarter  exptime  distance  year  \\\n",
+       "KeplerSearch object containing 10 data products \n",
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        "0  kplr007419318   Kepler  Kepler        7     60.0       0.0  2010   \n",
        "1  kplr007419318   Kepler  Kepler        7     60.0       0.0  2010   \n",
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-       "4  Target Pixel Short Cadence (TPS) - Q7  \n",
-       "5    Lightcurve Short Cadence (CSC) - Q7  \n",
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    "metadata": {},
    "outputs": [
     {
      "data": {
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-       "K2Search object containing 6 data products<div>\n",
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-       "K2Search object containing 6 data products     target_name  pipeline mission campaign  exptime  distance  year  \\\n",
+       "K2Search object containing 6 data products \n",
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        "0  ktwo201912552        K2      K2        1   1800.0       0.0  2014   \n",
        "1  ktwo201912552        K2      K2        1   1800.0       0.0  2014   \n",
        "2  ktwo201912552   EVEREST    HLSP        1   1800.0       0.0  2014   \n",
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        "5  ktwo201912552  K2VARCAT    HLSP        1   1800.0       0.0  2014   \n",
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-       "1    Lightcurve Long Cadence (KLC) - C01  \n",
-       "2                                   FITS  \n",
-       "3                                    PDF  \n",
+       "0    Lightcurve Long Cadence (KLC) - C01  \n",
+       "1  Target Pixel Long Cadence (KTL) - C01  \n",
+       "2                                    PDF  \n",
+       "3                                   FITS  \n",
        "4                                   FITS  \n",
        "5                                   FITS  "
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      "metadata": {},
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@@ -4287,14 +4364,15 @@
   },
   {
    "cell_type": "code",
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    "id": "7d27bf57",
    "metadata": {},
    "outputs": [
     {
      "data": {
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-       "K2Search object containing 4 data products<div>\n",
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-       "K2Search object containing 4 data products     target_name  pipeline mission campaign  exptime  distance  year  \\\n",
+       "K2Search object containing 4 data products \n",
+       "     target_name  pipeline mission campaign  exptime  distance  year  \\\n",
        "0  ktwo201912552   EVEREST    HLSP        1   1800.0       0.0  2014   \n",
        "1  ktwo201912552   EVEREST    HLSP        1   1800.0       0.0  2014   \n",
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        "\n",
        "  description  \n",
-       "0        FITS  \n",
-       "1         PDF  \n",
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        "3        FITS  "
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-     "execution_count": 28,
+     "execution_count": 29,
      "metadata": {},
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@@ -4398,7 +4477,7 @@
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   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "id": "c2cd7ee3",
    "metadata": {},
    "outputs": [
@@ -4406,21 +4485,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "pipeline products: 100%|██████████████████████████| 3/3 [00:01<00:00,  2.50it/s]"
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-       "   index                                         Local Path    Status Message  \\\n",
-       "0      0  /Users/tapritc2/.lksearch/cache/mastDownload/H...  COMPLETE    None   \n",
-       "1      0  /Users/tapritc2/.lksearch/cache/mastDownload/H...  COMPLETE    None   \n",
-       "2      0  /Users/tapritc2/.lksearch/cache/mastDownload/H...  COMPLETE    None   \n",
-       "\n",
-       "    URL  \n",
-       "0  None  \n",
-       "1  None  \n",
-       "2  None  "
+       "                                          Local Path    Status Message   URL\n",
+       "0  /Users/tapritc2/.lksearch/cache/mastDownload/H...  COMPLETE    None  None\n",
+       "1  /Users/tapritc2/.lksearch/cache/mastDownload/H...  COMPLETE    None  None\n",
+       "2  /Users/tapritc2/.lksearch/cache/mastDownload/H...  COMPLETE    None  None"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4528,7 +4584,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "id": "5ebda13a-0051-48c7-8272-e6aa3040f4b2",
    "metadata": {},
    "outputs": [
@@ -4538,7 +4594,7 @@
        "'/Users/tapritc2/.lksearch/cache'"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4559,7 +4615,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "id": "6e6625e2-c1c2-40fb-8fd2-fa3633cf3722",
    "metadata": {},
    "outputs": [],
@@ -4580,7 +4636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "id": "1345bf88-74be-4aea-96c9-512ab31b349b",
    "metadata": {},
    "outputs": [
@@ -4622,7 +4678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "id": "345bb5e5-7b13-4ee8-9a08-c85de036ace4",
    "metadata": {},
    "outputs": [],
@@ -4640,7 +4696,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "id": "be7bc33c-eb6c-4ce7-81f8-d98076581cc4",
    "metadata": {},
    "outputs": [
@@ -4650,7 +4706,7 @@
        "'/Users/tapritc2/.lksearch/config'"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4661,7 +4717,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "id": "341857e6-aa78-4cc6-b68d-79fca23f7e63",
    "metadata": {},
    "outputs": [
@@ -4671,7 +4727,7 @@
        "'/Users/tapritc2/.lksearch/config/lksearch.cfg'"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4697,7 +4753,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/src/lksearch/K2Search.py b/src/lksearch/K2Search.py
index 1026dfa..ed687d4 100644
--- a/src/lksearch/K2Search.py
+++ b/src/lksearch/K2Search.py
@@ -78,10 +78,16 @@ def __init__(
         exptime: Optional[Union[str, int, tuple]] = (0, 9999),
         pipeline: Optional[Union[str, list[str]]] = None,
         campaign: Optional[Union[int, list[int]]] = None,
+        hlsp: bool = True,
     ):
+        if hlsp is False:
+            pipeline = ["K2"]
+            self.mission_search = ["K2"]
+        else:
+            self.mission_search = ["K2", "HLSP"]
         super().__init__(
             target=target,
-            mission=["K2"],
+            mission=self.mission_search,
             obs_table=obs_table,
             prod_table=prod_table,
             table=table,
@@ -152,39 +158,69 @@ def _sort_K2(self):
 
     def filter_table(
         self,
-        limit: int = None,
-        exptime: Union[int, float, tuple, type(None)] = None,
+        target_name: Union[str, list[str]] = None,
         pipeline: Union[str, list[str]] = None,
+        mission: Union[str, list[str]] = None,
+        exptime: Union[int, float, tuple[float]] = None,
+        distance: Union[float, tuple[float]] = None,
+        year: Union[int, list[int], tuple[int]] = None,
+        description: Union[str, list[str]] = None,
+        filetype: Union[str, list[str]] = None,
         campaign: Union[int, list] = None,
+        limit: int = None,
+        inplace=False,
+        **kwargs,
     ):
         """
         Filters the search result table by specified parameters
 
         Parameters
         ----------
-        limit : int, optional
-            limit to the number of results, by default None
-        exptime : Union[int, float, tuple, type, optional
-            exposure times to filter by, by default None
-        pipeline : Union[str, list[str]], optional
-            pipeline used for data reduction, by default None
+        target_name : str, optional
+            Name of targets. A list will look for multiple target names.
+        pipeline : str or list[str]], optional
+            Data pipeline.  A list will look for multiple pipelines.
+        mission : str or list[str]], optional
+            Mission. A list will look for muliple missions.
+        exptime : int or float, tuple[float]], optional
+            Exposure Time. A tuple will look for a range of times.
+        distance : float or tuple[float]], optional
+            Distance. A float searches for products with a distance less than the value given,
+            a tuple will search between the given values.
+        year : int or list[int], tuple[int]], optional
+            Year. A list will look for multiple years, a tuple will look in the range of years.
+        description : str or list[str]], optional
+            Description of product. A list will look for descriptions containing any keywords given,
+            a tuple will look for descriptions containing all the keywords.
+        filetype : str or list[str]], optional
+            Type of product. A list will look for multiple filetypes.
         campaign : Optional[int], optional
-            K2 observing campaign(s), by default None
+            K2 observing campaign, by default None
+        limit : int, optional
+            how many rows to return, by default None
+        inplace : bool, optional
+            whether to modify the KeplerSearch inplace, by default False
 
         Returns
         -------
-        K2Search object with updated table.
+        K2Search object with updated table or None if `inplace==True`
         """
-        mask = np.ones(len(self.table), dtype=bool)
-
-        if exptime is not None:
-            mask = mask & self._mask_by_exptime(exptime)
-        if pipeline is not None:
-            mask = mask & self.table["pipeline"].isin(np.atleast_1d(pipeline))
-        if campaign is not None:
-            mask = mask & self.table["sequence_number"].isin(np.atleast_1d(campaign))
+        mask = self._filter(
+            target_name=target_name,
+            filetype=filetype,
+            exptime=exptime,
+            distance=distance,
+            year=year,
+            description=description,
+            pipeline=pipeline,
+            mission=mission,
+            sequence_number=campaign,
+        )
         if limit is not None:
             cusu = np.cumsum(mask)
             if max(cusu) > limit:
                 mask = mask & (cusu <= limit)
-        return self._mask(mask)
+        if inplace:
+            self.table = self.table[mask].reset_index()
+        else:
+            return self._mask(mask)
diff --git a/src/lksearch/KeplerSearch.py b/src/lksearch/KeplerSearch.py
index 629bb37..47a61d2 100644
--- a/src/lksearch/KeplerSearch.py
+++ b/src/lksearch/KeplerSearch.py
@@ -81,10 +81,17 @@ def __init__(
         pipeline: Optional[Union[str, list[str]]] = None,
         quarter: Optional[Union[int, list[int]]] = None,
         month: Optional[int] = None,
+        hlsp: bool = True,
     ):
+        if hlsp is False:
+            pipeline = ["Kepler"]
+            self.mission_search = ["Kepler"]
+        else:
+            self.mission_search = ["Kepler", "HLSP"]
+
         super().__init__(
             target=target,
-            mission=["Kepler"],
+            mission=self.mission_search,
             obs_table=obs_table,
             prod_table=prod_table,
             table=table,
@@ -236,42 +243,75 @@ def _sort_Kepler(self):
 
     def filter_table(
         self,
-        limit: int = None,
-        exptime: Union[int, float, tuple, type(None)] = None,
+        target_name: Union[str, list[str]] = None,
         pipeline: Union[str, list[str]] = None,
+        mission: Union[str, list[str]] = None,
+        exptime: Union[int, float, tuple[float]] = None,
+        distance: Union[float, tuple[float]] = None,
+        year: Union[int, list[int], tuple[int]] = None,
+        description: Union[str, list[str]] = None,
+        filetype: Union[str, list[str]] = None,
+        limit: int = None,
+        inplace=False,
         quarter: Optional[int] = None,
         month: Optional[int] = None,
+        **kwargs,
     ):
         """
         Filters the search result table by specified parameters
 
         Parameters
         ----------
-        limit : int, optional
-            limit to the number of results, by default None
-        exptime : Union[int, float, tuple, type, optional
-            exposure times to filter by, by default None
-        pipeline : Union[str, list[str]], optional
-            pipeline used for data reduction, by default None
+        target_name : str, optional
+            Name of targets. A list will look for multiple target names.
+        pipeline : str or list[str]], optional
+            Data pipeline.  A list will look for multiple pipelines.
+        mission : str or list[str]], optional
+            Mission. A list will look for muliple missions.
+        exptime : int or float, tuple[float]], optional
+            Exposure Time. A tuple will look for a range of times.
+        distance : float or tuple[float]], optional
+            Distance. A float searches for products with a distance less than the value given,
+            a tuple will search between the given values.
+        year : int or list[int], tuple[int]], optional
+            Year. A list will look for multiple years, a tuple will look in the range of years.
+        description : str or list[str]], optional
+            Description of product. A list will look for descriptions containing any keywords given,
+            a tuple will look for descriptions containing all the keywords.
+        filetype : str or list[str]], optional
+            Type of product. A list will look for multiple filetypes.
         quarter : Optional[int], optional
             Kepler observing quarter, by default None
         month : Optional[int], optional
             Kepler observing month, by default None
+        limit : int, optional
+            how many rows to return, by default None
+        inplace : bool, optional
+            whether to modify the KeplerSearch inplace, by default False
 
         Returns
         -------
-        KeplerSearch object with updated table.
+        KeplerSearch object with updated table or None if `inplace==True`
         """
         mask = np.ones(len(self.table), dtype=bool)
+        mask = self._filter(
+            target_name=target_name,
+            filetype=filetype,
+            exptime=exptime,
+            distance=distance,
+            year=year,
+            description=description,
+            pipeline=pipeline,
+            mission=mission,
+        )
 
-        if exptime is not None:
-            mask = mask & self._mask_by_exptime(exptime)
-        if pipeline is not None:
-            mask = mask & self.table["pipeline"].isin(np.atleast_1d(pipeline))
         if (quarter is not None) | (month is not None):
             mask = mask & self._filter_kepler(quarter=quarter, month=month)
         if limit is not None:
             cusu = np.cumsum(mask)
             if max(cusu) > limit:
                 mask = mask & (cusu <= limit)
-        return self._mask(mask)
+        if inplace:
+            self.table = self.table[mask].reset_index()
+        else:
+            return self._mask(mask)
diff --git a/src/lksearch/MASTSearch.py b/src/lksearch/MASTSearch.py
index ef245ca..ce751b9 100644
--- a/src/lksearch/MASTSearch.py
+++ b/src/lksearch/MASTSearch.py
@@ -109,13 +109,16 @@ def __init__(
 
         if isinstance(table, type(None)):
             self._searchtable_from_target(target)
-            self.table = self._update_table(self.table)
             self.table = self._fix_table_times(self.table)
 
         # If MAST search tables are provided, another MAST search is not necessary
         else:
             self._searchtable_from_table(table, obs_table, prod_table)
 
+        for col in self.table.columns:
+            if not hasattr(self, col):
+                setattr(self, col, self.table[col])
+
     def __len__(self):
         """Returns the number of products in the SearchResult table."""
         return len(self.table)
@@ -267,23 +270,29 @@ def _searchtable_from_target(self, target: Union[str, tuple[float], SkyCoord]):
         None - sets self.table equal to the masked/filtered joint table
         """
         self._parse_input(target)
-        seq = self.search_sequence
 
         self.table = self._search(
             search_radius=self.search_radius,
             exptime=self.search_exptime,
             mission=self.search_mission,
             pipeline=self.search_pipeline,
-            sequence=seq,
+            sequence=self.search_sequence,
         )
+        self.table = self._update_table(self.table)
+
+        filetype = [
+            "target pixel",
+            "lightcurve",
+            "dvreport",
+        ]
         mask = self._filter(
             exptime=self.search_exptime,
             mission=self.search_mission,
             pipeline=self.search_pipeline,
-            sequence=self.search_sequence,
-        )  # setting provenance_name=None will return HLSPs
-
-        self.table = self.table[mask]
+            sequence_number=self.search_sequence,
+            filetype=filetype,
+        )
+        self.table = self.table[mask].reset_index(drop=True)
 
     def _searchtable_from_table(
         self,
@@ -326,6 +335,7 @@ def _searchtable_from_table(
         else:
             raise (ValueError("No Target or object table supplied"))
 
+    # def _downsize_table(self, ds_table):
     def _mask(self, mask):
         """Masks down the product and observation tables given an input mask, then returns them as a new Search object.
         deepcopy is used to preserve the class metadata stored in class variables"""
@@ -797,102 +807,6 @@ def _mask_product_type(
             mask |= self.table.productFilename.str.endswith(value)
         return mask
 
-    def _filter(
-        self,
-        exptime: Union[str, int, tuple[int], type(None)] = (0, 9999),
-        mission: Union[str, list[str]] = ["Kepler", "K2", "TESS"],
-        pipeline: Union[str, list[str]] = ["kepler", "k2", "spoc"],
-        filetype: Union[str, list[str]] = [
-            "target pixel",
-            "lightcurve",
-            "dvreport",
-        ],
-        sequence: Union[int, list[int]] = None,
-    ) -> pd.DataFrame:
-        """filter self.table based on your product search preferences
-
-        Parameters
-        ----------
-        exptime : Union[str, int, tuple[int], type, optional
-            exposure time of data products to search, by default (0, 9999)
-        project : Union[str, list[str]], optional
-            mission project to search, by default ["Kepler", "K2", "TESS"]
-        pipeline : Union[str, list[str]], optional
-            pipeline provinence to search for data from, by default ["kepler", "k2", "spoc"]
-        filetype : Union[str, list[str]], optional
-            file types to search for, by default [ "target pixel", "lightcurve", "dvreport", ]
-        sequence : Union[int, list[int]], optional
-            sequence number to filter by. Corresponds to sector for TESS, campaign for K2, and quarter for Kepler
-        Returns
-        -------
-        mask
-            cumulative boolean mask for self.table based off of
-            the product of individual filter properties
-        """
-
-        """ Modify this so that it can choose what types of products to keep?
-        Since this will be used by mission specific search we want this to filter:
-        Filetype
-        ExposureTime/cadence
-        Pipe(Provenance)/Project - e.g. (SPOC/TESSSpoc)
-        <Segment/Quarter/Sector/Campaign> this will be in mission specific search
-        """
-
-        self.search_exptime = exptime
-        mask = np.zeros(len(self.table), dtype=bool)
-
-        # First filter on filetype
-        file_mask = mask.copy()
-
-        # This is the list of allowed filetypes we can interact with
-        allowed_ftype = ["lightcurve", "target pixel", "dvreport"]
-
-        filter_ftype = [
-            file.lower() for file in filetype if file.lower() in allowed_ftype
-        ]
-        # First filter on filetype
-
-        if len(filter_ftype) == 0:
-            filter_ftype = allowed_ftype
-            log.warning("Invalid filetype filtered. Returning all data.")
-
-        file_mask = mask.copy()
-        for ftype in filter_ftype:
-            file_mask |= self._mask_product_type(ftype)
-
-        # Next Filter on mission
-        mission_mask = mask.copy()
-        if not isinstance(mission_mask, type(None)):
-            for m in mission:
-                mission_mask |= self.table.project_obs.values == m
-        else:
-            mission_mask = np.logical_not(mission_mask)
-
-        # Next Filter on pipeline (provenance_name in mast table)
-        provenance_mask = mask.copy()
-        if not isinstance(pipeline, type(None)):
-            for a in np.atleast_1d(pipeline).tolist():
-                provenance_mask |= self.table.provenance_name.str.lower() == a.lower()
-        else:
-            provenance_mask = np.logical_not(provenance_mask)
-
-        # Filter by cadence
-        if not isinstance(exptime, type(None)):
-            exptime_mask = self._mask_by_exptime(exptime)
-        else:
-            exptime_mask = not mask
-
-        # Filter by sequence
-        sequence_mask = mask.copy()
-        if not isinstance(sequence, type(None)):
-            for s in np.atleast_1d(sequence).tolist():
-                sequence_mask |= self.table.sequence_number == s
-        else:
-            sequence_mask = np.logical_not(sequence_mask)
-
-        mask = file_mask & mission_mask & provenance_mask & exptime_mask & sequence_mask
-        return mask
-
     def _mask_by_exptime(self, exptime: Union[int, tuple[float]]):
         """Helper function to filter by exposure time.
         Returns a boolean array"""
@@ -920,26 +834,264 @@ def _mask_by_exptime(self, exptime: Union[int, tuple[float]]):
             else:
                 mask = np.ones(len(exposures), dtype=bool)
                 log.debug("invalid string input. No exptime filter applied")
+        elif isinstance(exptime, list):
+            mask = np.zeros(len(exposures), dtype=bool)
+            for et in exptime:
+                mask = mask | (exposures == et)
+
         return mask
 
-    def filter_table(
+    def query_table(
         self,
-        # Filter the table by keywords
-        limit: int = None,
-        exptime: Union[int, float, tuple, type(None)] = None,
+        criteria: str,
+        inplace: bool = False,
+        **kwargs,
+    ):
+        """Filter the Search Result table using pandas query
+        https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.query.html
+
+        Parameters
+        ----------
+        criteria : str
+            string containing criteria to filter table. Can handle multiple criteria,
+            e.g. "exptime>=100 and exptime<=500".
+        inplace : bool
+            if True, modify table in MASTSearch object directly. If False, returns a
+            new MASTSearch object with the resulting table
+
+        Returns
+        -------
+        MASTSearch : MASTSearch object
+            Only returned if inplace = False
+        """
+        filtered_table = self.table.query(criteria).reset_index()
+        if inplace:
+            self.table = filtered_table
+        else:
+            new_MASTSearch = deepcopy(self)
+            new_MASTSearch.table = filtered_table
+            return new_MASTSearch
+
+    def _filter(
+        self,
+        target_name: Union[str, list[str]] = None,
         pipeline: Union[str, list[str]] = None,
+        mission: Union[str, list[str]] = None,
+        exptime: Union[int, float, tuple[float]] = None,
+        distance: Union[float, tuple[float]] = None,
+        year: Union[int, list[int], tuple[int]] = None,
+        description: Union[str, list[str], tuple[str]] = None,
+        filetype: Union[str, list[str]] = None,
+        sequence_number: Union[int, list[int]] = None,
     ):
+        """filter self.table based on your product search preferences
+
+        Parameters
+        ----------
+        target_name : str, optional
+            Name of targets.
+        pipeline : str or list[str]], optional
+            Pipeline provenance to search for data from.
+        mission : str or list[str], optional
+            Mission projects to search, options are "Kepler", "K2", and "TESS".
+        exptime : int or float, tuple[float]], optional
+            Exposure time of data products to search.
+            An int, float, or list will look for exact matches,
+            a tuple will look for a range of times,
+            and string values "fast", "short", "long", "shortest", and "longest"
+            will match the appropriate exposure times.
+        distance : float or tuple[float]], optional
+            Distance from given search target to get data products.
+            A float searches for products within the given distance,
+            a tuple searches between the given values.
+        year : int or list[int], tuple[int], optional
+            Year of creation.
+            A list will look for all years given,
+            a tuple will look in the range of years given.
+        description : str or list[str], optional
+            Key words to search for in the description of the data product.
+            A list will look for descriptions containing any keywords given,
+            a tuple will look for descriptions containing all the keywords.
+        filetype : str or list[str], optional
+            file types to search for, options are "target pixel", "lightcurve", "dvreport".
+        sequence_number : int or list[int]], optional
+            Sequence number to filter by. Corresponds to sector for TESS, campaign for K2, and quarter for Kepler.
+
+        Returns
+        -------
+        mask : np.ndarray
+            cumulative boolean mask for self.table based off of
+            the product of individual filter properties
+        """
         mask = np.ones(len(self.table), dtype=bool)
+        if target_name is not None:
+            target_name = np.atleast_1d(target_name).astype(str)
+            mask = mask & self.table["target_name"].isin(target_name)
+
+        if not isinstance(filetype, type(None)):
+            allowed_ftype = ["lightcurve", "target pixel", "dvreport"]
+            filter_ftype = [
+                file.lower()
+                for file in np.atleast_1d(filetype).astype(str)
+                if file.lower() in allowed_ftype
+            ]
+            if len(filter_ftype) == 0:
+                filter_ftype = allowed_ftype
+                log.warning("Invalid filetype filtered. Returning all filetypes.")
+
+            file_mask = np.zeros(len(self.table), dtype=bool)
+            for ftype in filter_ftype:
+                file_mask |= self._mask_product_type(ftype)
+            mask = mask & file_mask
 
         if exptime is not None:
             mask = mask & self._mask_by_exptime(exptime)
+
+        if distance is not None:
+            if isinstance(distance, float):
+                mask = mask & self.table.eval("distance <= @distance")
+            elif isinstance(distance, tuple):
+                mask = mask & self.table.eval(
+                    "(distance >= @distance[0]) & (distance <= @distance[1])"
+                )
+            else:
+                log.warning(
+                    "Invalid input for `distance`, allowed inputs are float and tuple. Ignoring `distance` search parameter."
+                )
+
+        if year is not None:
+            if isinstance(year, str):
+                year = int(year)
+            if hasattr(year, "__iter__"):
+                year_type = type(year)
+                year = [int(y) for y in year]
+                year = year_type(year)
+            if (
+                isinstance(year, np.int_)
+                or isinstance(year, int)
+                or isinstance(year, list)
+            ):
+                mask = mask & self.table["year"].isin(np.atleast_1d(year))
+            elif isinstance(year, tuple):
+                mask = mask & self.table.eval("year>=@year[0] & year<=@year[1]")
+            else:
+                log.warning(
+                    "Invalid input for `year`, allowed inputs are str, int, and tuple. Ignoring `year` search parameter."
+                )
+
         if pipeline is not None:
-            mask = mask & self.table["pipeline"].isin(np.atleast_1d(pipeline))
+            pipeline = list(map(str.lower, np.atleast_1d(pipeline).astype(str)))
+            mask = mask & self.table["pipeline"].str.lower().isin(pipeline)
+
+        if description is not None:
+            if isinstance(description, str):
+                mask = mask & self.table["description"].str.lower().str.contains(
+                    description.lower()
+                )
+            elif isinstance(description, tuple):
+                # Looks for descriptions which contain *all* of the given keywords
+                for word in description:
+                    mask = mask & self.table["description"].str.lower().str.contains(
+                        word.lower()
+                    )
+            elif hasattr(description, "__iter__"):
+                # Looks for descriptions which contain *any* of the given keywords
+                desc_mask = np.zeros(len(self.table), dtype=bool)
+                for word in description:
+                    desc_mask = desc_mask | self.table[
+                        "description"
+                    ].str.lower().str.contains(word.lower())
+                mask = mask & desc_mask
+            else:
+                log.warning(
+                    "Invalid input for `description`, allowed inputs are str and list[str]. Ignoring `description` search parameter."
+                )
+
+        if sequence_number is not None:
+            mask = mask & self.table.sequence_number.isin(
+                np.atleast_1d(sequence_number)
+            )
+
+        if mission is not None:
+            mission = list(map(str.lower, np.atleast_1d(mission).astype(str)))
+            mask = mask & self.table["mission"].str.lower().isin(mission)
+
+        return mask
+
+    def filter_table(
+        self,
+        target_name: Union[str, list[str]] = None,
+        pipeline: Union[str, list[str]] = None,
+        mission: Union[str, list[str]] = None,
+        exptime: Union[int, float, tuple[float]] = None,
+        distance: Union[float, tuple[float]] = None,
+        year: Union[int, list[int], tuple[int]] = None,
+        description: Union[str, list[str]] = None,
+        filetype: Union[str, list[str]] = None,
+        limit: int = None,
+        inplace=False,
+        **kwargs,
+    ):
+        """Filter the search by keywords
+
+        Parameters
+        ----------
+        target_name : str, optional
+            Name of targets. A list will look for multiple target names.
+        pipeline : str or list[str]], optional
+            Data pipeline.  A list will look for multiple pipelines.
+        mission : str or list[str]], optional
+            Mission. A list will look for muliple missions.
+        exptime : int or float, tuple[float]], optional
+            Exposure Time. A tuple will look for a range of times.
+        distance : float or tuple[float]], optional
+            Distance. A float searches for products with a distance less than the value given,
+            a tuple will search between the given values.
+        year : int or list[int], tuple[int]], optional
+            Year. A list will look for multiple years, a tuple will look in the range of years.
+        description : str or list[str]], optional
+            Description of product. A list will look for descriptions containing any keywords given,
+            a tuple will look for descriptions containing all the keywords.
+        filetype : str or list[str]], optional
+            Type of product. A list will look for multiple filetypes.
+        sequence : int or list[int]], optional
+            Sequence number refers to "quarter" for Kepler, "campaign" for K2, and "sector" for TESS.
+        limit : int, optional
+            _description_, by default None
+        inplace : bool, optional
+            _description_, by default False
+
+        Returns
+        -------
+        MASTSearch or None
+            Returns a filtered MASTSearch object or None if `inplace=True`
+        """
+        if "sequence" in kwargs:
+            sequence = kwargs["sequence"]
+        else:
+            sequence = None
+
+        mask = self._filter(
+            target_name=target_name,
+            pipeline=pipeline,
+            mission=mission,
+            exptime=exptime,
+            distance=distance,
+            year=year,
+            description=description,
+            filetype=filetype,
+            sequence_number=sequence,
+        )
+
         if limit is not None:
             cusu = np.cumsum(mask)
             if max(cusu) > limit:
                 mask = mask & (cusu <= limit)
-        return self._mask(mask)
+
+        if inplace:
+            self.table = self.table[mask].reset_index()
+        else:
+            return self._mask(mask)
 
     @suppress_stdout
     def _download_one(
@@ -968,7 +1120,6 @@ def _download_one(
             if pd.notna(row["cloud_uri"]):
                 download = False
         if conf.DOWNLOAD_CLOUD or download:
-            print(cloud_only)
             manifest = Observations.download_products(
                 Table().from_pandas(row.to_frame(name=" ").transpose()),
                 download_dir=download_dir,
diff --git a/src/lksearch/TESSSearch.py b/src/lksearch/TESSSearch.py
index 3ca833e..e716191 100644
--- a/src/lksearch/TESSSearch.py
+++ b/src/lksearch/TESSSearch.py
@@ -88,10 +88,13 @@ def __init__(
     ):
         if hlsp is False:
             pipeline = ["SPOC", "TESS-SPOC", "TESScut"]
+            self.mission_search = ["TESS"]
+        else:
+            self.mission_search = ["TESS", "HLSP"]
 
         super().__init__(
             target=target,
-            mission=["TESS"],
+            mission=self.mission_search,
             obs_table=obs_table,
             prod_table=prod_table,
             table=table,
@@ -100,6 +103,7 @@ def __init__(
             pipeline=pipeline,
             sequence=sector,
         )
+
         if table is None:
             if ("TESScut" in np.atleast_1d(pipeline)) or (type(pipeline) is type(None)):
                 self._add_tesscut_products(sector)
@@ -289,29 +293,19 @@ def _sort_TESS(self):
         )
         self.table = df
 
-    def search_individual_ffi(
+    def search_sector_ffis(
         self,
-        tmin: Union[float, Time],
-        tmax: Union[float, Time],
-        search_radius: Union[float, u.Quantity] = 0.0001 * u.arcsec,
-        exptime: Union[str, int, tuple] = (0, 9999),
-        sector: Union[int, type[None]] = None,
+        # tmin: Union[float, Time, tuple] = None,
+        # tmax: Union[float, Time, tuple] = None,
+        # search_radius: Union[float, u.Quantity] = 0.0001 * u.arcsec,
+        # exptime: Union[str, int, tuple] = (0, 9999),
+        sector: Union[int, type[None]],  # = None,
         **extra_query_criteria,
     ):
-        """Search for a particular FFI file given a time range, return the product list
-        of FFIs for that this target and time range
-
+        """Returns a list of the FFIs available in a particular sector
 
         Parameters
         ----------
-        tmin : Union[float,Time]
-            minimum start time of the FFI
-        tmax : Union[float,Time]
-            maximum end time of the ffi
-        search_radius : Union[float, u.Quantity], optional
-            radius around target to search for FFIs, by default 0.0001*u.arcsec
-        exptime : Union[str, int, tuple], optional
-            exposure time of FFI's to search for, by default (0, 9999)
         sector : Union[int, type[None]]
             sector(s) in which to search for FFI files, by default None
 
@@ -325,29 +319,9 @@ def search_individual_ffi(
         query_criteria["provenance_name"] = "SPOC"
         query_criteria["dataproduct_type"] = "image"
 
-        if type(tmin) == type(Time):
-            tmin = tmin.mjd
-
-        if type(tmax) == type(Time):
-            tmax = tmax.mjd
-
-        query_criteria["t_min"] = (tmin, tmax)
-        query_criteria["t_max"] = (tmin, tmax)
-
-        if not isinstance(search_radius, u.quantity.Quantity):
-            log.warning(
-                f"Search radius {search_radius} units not specified, assuming arcsec"
-            )
-            search_radius = search_radius * u.arcsec
-
         if sector is not None:
             query_criteria["sequence_number"] = sector
 
-        if exptime is not None:
-            query_criteria["t_exptime"] = exptime
-
-        query_criteria["radius"] = str(search_radius.to(u.deg))
-
         ffi_obs = Observations.query_criteria(
             objectname=self.target_search_string,
             **query_criteria,
@@ -385,46 +359,81 @@ def search_individual_ffi(
         new_table.table["sector"] = new_table.table["obs_id"].apply(
             lambda x: int(x.split("-")[1][1:])
         )
+        new_table.table["t_min"] = pd.NA * len(new_table.table)
+        new_table.table["t_max"] = pd.NA * len(new_table.table)
+
         return new_table
 
     def filter_table(
         self,
-        limit: int = None,
-        exptime: Union[int, float, tuple, type(None)] = None,
+        target_name: Union[str, list[str]] = None,
         pipeline: Union[str, list[str]] = None,
-        sector: Union[int, list[int]] = None,
+        mission: Union[str, list[str]] = None,
+        exptime: Union[int, float, tuple[float]] = None,
+        distance: Union[float, tuple[float]] = None,
+        year: Union[int, list[int], tuple[int]] = None,
+        description: Union[str, list[str]] = None,
+        filetype: Union[str, list[str]] = None,
+        limit: int = None,
+        inplace=False,
+        sector: Union[int, list[str]] = None,
+        **kwargs,
     ):
         """
         Filters the search result table by specified parameters
 
         Parameters
         ----------
-        limit : int, optional
-            limit to the number of results, by default None
-        exptime : Union[int, float, tuple, type, optional
-            exposure times to filter by, by default None
-        pipeline : Union[str, list[str]], optional
-            pipeline used for data reduction, by default None
+        target_name : str, optional
+            Name of targets. A list will look for multiple target names.
+        pipeline : str or list[str]], optional
+            Data pipeline.  A list will look for multiple pipelines.
+        mission : str or list[str]], optional
+            Mission. A list will look for muliple missions.
+        exptime : int or float, tuple[float]], optional
+            Exposure Time. A tuple will look for a range of times.
+        distance : float or tuple[float]], optional
+            Distance. A float searches for products with a distance less than the value given,
+            a tuple will search between the given values.
+        year : int or list[int], tuple[int]], optional
+            Year. A list will look for multiple years, a tuple will look in the range of years.
+        description : str or list[str]], optional
+            Description of product. A list will look for descriptions containing any keywords given,
+            a tuple will look for descriptions containing all the keywords.
+        filetype : str or list[str]], optional
+            Type of product. A list will look for multiple filetypes.
         sector : Optional[int], optional
-            TESS observing sector(s), by default None
+            TESS observing sector, by default None
+        limit : int, optional
+            how many rows to return, by default None
+        inplace : bool, optional
+            whether to modify the KeplerSearch inplace, by default False
 
         Returns
         -------
-        TESSSearch object with updated table
+        TESSSearch object with updated table or None if `inplace==True`
         """
-        mask = np.ones(len(self.table), dtype=bool)
+        mask = self._filter(
+            target_name=target_name,
+            filetype=filetype,
+            exptime=exptime,
+            distance=distance,
+            year=year,
+            description=description,
+            pipeline=pipeline,
+            sequence_number=sector,
+            mission=mission,
+        )
 
-        if exptime is not None:
-            mask = mask & self._mask_by_exptime(exptime)
-        if pipeline is not None:
-            mask = mask & self.table["pipeline"].isin(np.atleast_1d(pipeline))
-        if sector is not None:
-            mask = mask & self.table["sequence_number"].isin(np.atleast_1d(sector))
         if limit is not None:
             cusu = np.cumsum(mask)
             if max(cusu) > limit:
                 mask = mask & (cusu <= limit)
-        return self._mask(mask)
+
+        if inplace:
+            self.table = self.table[mask].reset_index()
+        else:
+            return self._mask(mask)
 
     def download(
         self,
diff --git a/tests/test_search.py b/tests/test_search.py
index 4a34d82..0a041ea 100644
--- a/tests/test_search.py
+++ b/tests/test_search.py
@@ -429,24 +429,120 @@ def test_split_k2_campaigns():
     assert search_c11.table["campaign"][1] == "11b"
 
 
-def test_tesscut():
-    """Can we find TESS tesscut tpfs"""
+def test_FFI_retrieval():
+    """Can we find TESS individual FFI's"""
     target = "Kepler 16b"
-    assert (
-        len(TESSSearch("Kepler 16b").search_individual_ffi(58682, 58710, sector=14))
-        == 1281
-    )
+    assert len(TESSSearch("Kepler 16b").search_sector_ffis(14)) == 1241
 
 
 def test_tesscut():
     """Can we find and download TESS tesscut tpfs"""
     results = TESSSearch("Kepler 16b", hlsp=False, sector=14)
-    assert len(results) == 11
-    assert len(results.cubedata) == 3
-    manifest = results.cubedata[2].download()
+    assert len(results) == 9
+    assert len(results.cubedata) == 2
+    manifest = results.cubedata[1].download()
     assert len(manifest) == 1
 
 
+class TestMASTSearchFilter:
+    results = MASTSearch("Kepler 16b")
+
+    @pytest.mark.parametrize(
+        "target_name",
+        (
+            0,
+            299096355,
+            "kplr012644769",
+            [299096355, "kplr012644769"],
+            [299096355, "299096355"],
+        ),
+    )
+    def test_target_name(self, target_name):
+        filter_results = self.results.filter_table(target_name=target_name)
+
+    @pytest.mark.parametrize("limit", (0, 10, 1000))
+    def test_limit(self, limit):
+        filter_results = self.results.filter_table(limit=limit)
+
+    @pytest.mark.parametrize("filetype", (0, "lightcurve"))
+    def test_filetype(self, filetype):
+        filter_results = self.results.filter_table(filetype=filetype)
+
+    @pytest.mark.parametrize(
+        "exptime",
+        (
+            0,
+            20,
+            20.0,
+            [0, 20],
+            [20, 60.0],
+            (0, 100),
+            "fast",
+            "short",
+            "long",
+            "shortest",
+            "longest",
+        ),
+    )
+    def test_exptime(self, exptime):
+        filter_results = self.results.filter_table(exptime=exptime)
+
+    @pytest.mark.parametrize("distance", (0, 0.2, (0.2, 0.4)))
+    def test_distance(self, distance):
+        filter_results = self.results.filter_table(distance=distance)
+
+    @pytest.mark.parametrize("year", (0, 2013, (2000, 2020), [2013, 2019]))
+    def test_year(self, year):
+        filter_results = self.results.filter_table(year=year)
+
+    @pytest.mark.parametrize(
+        "description", (0, "data", ["TPS", "report"], ("TPS", "report"))
+    )
+    def test_description(self, description):
+        filter_results = self.results.filter_table(description=description)
+
+    @pytest.mark.parametrize("pipeline", (0, "Kepler", "spoc", ["kepler", "spoc"]))
+    def test_pipeline(self, pipeline):
+        filter_results = self.results.filter_table(pipeline=pipeline)
+
+    @pytest.mark.parametrize("sequence", (0, 14, [14, 15]))
+    def test_sequence(self, sequence):
+        filter_results = self.results.filter_table(sequence=sequence)
+
+    @pytest.mark.parametrize("mission", (0, "Kepler", "Tess", ["Kepler", "Tess"]))
+    def test_mission(self, mission):
+        filter_results = self.results.filter_table(mission=mission)
+
+    def test_combination(
+        self,
+    ):
+        filter_results = self.results.filter_table(
+            target_name=299096355,
+            pipeline="SPOC",
+            mission="TESS",
+            exptime=120,
+            distance=0.1,
+            year=2022,
+            description="light curves",
+            filetype="lightcurve",
+            sequence=55,
+            limit=10,
+        )
+        assert (
+            filter_results.table.obs_id.values[0]
+            == "tess2022217014003-s0055-0000000299096355-0242-s"
+        )
+
+
+def test_filter():
+    """Can we properly filter the data"""
+
+    results = TESSSearch("Kepler 16b")
+    filtered = results.filter_table(sector=14)
+    queried = results.query_table("sector == 14")
+    assert len(filtered) == len(queried)
+
+
 def test_tess_clouduris():
     """regression test - do tesscut/nan's in dataURI column break cloud uri fetching"""
     toi = TESSSearch("TOI 1161", sector=14)