diff --git a/chirp/projects/hoplite/index.py b/chirp/projects/hoplite/index.py
index 4775503e..7812cf99 100644
--- a/chirp/projects/hoplite/index.py
+++ b/chirp/projects/hoplite/index.py
@@ -42,18 +42,7 @@ def from_db(
       cls, db: interface.GraphSearchDBInterface, score_fn_name: str = 'dot'
   ) -> 'HopliteSearchIndex':
     """Create a VamanaSearchIndex from a GraphSearchDBInterface impl."""
-    # TODO(tomdenton): Use an enum for metric_name.
-    if score_fn_name in ('mip', 'dot'):
-      # mip == Max Inner Prouct
-      score_fn = score_functions.numpy_dot
-    elif score_fn_name in ('jax_mip', 'jax_dot'):
-      score_fn = score_functions.get_jax_dot()
-    elif score_fn_name == 'cosine':
-      score_fn = score_functions.numpy_cos
-    elif score_fn_name == 'euclidean':
-      score_fn = score_functions.numpy_euclidean
-    else:
-      raise ValueError(f'Unknown metric name: {score_fn_name}')
+    score_fn = score_functions.get_score_fn(score_fn_name)
     return cls(db, score_fn=score_fn)
 
   def initialize_index(self, out_degree: int, seed: int = 42) -> None:
diff --git a/chirp/projects/hoplite/score_functions.py b/chirp/projects/hoplite/score_functions.py
index acd5f2e0..43c444b7 100644
--- a/chirp/projects/hoplite/score_functions.py
+++ b/chirp/projects/hoplite/score_functions.py
@@ -27,8 +27,8 @@ def get_score_fn(
     score_fn = numpy_dot
   elif name == 'cos':
     score_fn = numpy_cos
-  elif name == 'euclidean':
-    score_fn = numpy_euclidean
+  elif name == 'neg_euclidean':
+    score_fn = numpy_neg_euclidean
   else:
     raise ValueError('Unknown score function: ', name)
 
@@ -38,7 +38,8 @@ def get_score_fn(
     bias_fn = score_fn
 
   if target_score is not None:
-    targeted_fn = lambda x, y: np.abs(bias_fn(x, y) - target_score)
+    # We want 'up is good', so take the negative absolute value.
+    targeted_fn = lambda x, y: -np.abs(bias_fn(x, y) - target_score)
   else:
     targeted_fn = bias_fn
 
@@ -67,18 +68,18 @@ def numpy_cos(data: np.ndarray, query: np.ndarray) -> np.ndarray:
   return np.dot(unit_data, unit_query)
 
 
-def numpy_euclidean(data: np.ndarray, query: np.ndarray) -> np.ndarray:
-  """Numpy L2 distance allowing multiple queries."""
+def numpy_neg_euclidean(data: np.ndarray, query: np.ndarray) -> np.ndarray:
+  """Negative L2 distance allowing multiple queries."""
   data_norms = np.linalg.norm(data, axis=-1)
   if len(query.shape) > 1:
     query_norms = np.linalg.norm(query, axis=-1)
     dot_products = np.tensordot(data, query, axes=(-1, -1))
     pairs = data_norms[:, np.newaxis] + query_norms[np.newaxis, :]
-    return pairs - 2 * dot_products
+    return -pairs + 2 * dot_products
 
   query_norm = np.linalg.norm(query)
   dot_products = np.dot(data, query)
-  return data_norms - 2 * dot_products + query_norm
+  return -data_norms + 2 * dot_products + query_norm
 
 
 def get_jax_dot():