diff --git a/spacy/vectors.pyx b/spacy/vectors.pyx
index 96ccff518..95378947a 100644
--- a/spacy/vectors.pyx
+++ b/spacy/vectors.pyx
@@ -10,7 +10,7 @@ cimport numpy as np
 from thinc.neural.util import get_array_module
 from thinc.neural._classes.model import Model
 
-from .strings cimport StringStore
+from .strings cimport StringStore, hash_string
 from .compat import basestring_, path2str
 from . import util
 
@@ -183,17 +183,42 @@ cdef class Vectors:
         for key, row in self.key2row.items():
             yield key, self.data[row]
 
-    def get_keys(self, rows):
-        xp = get_array_module(self.data)
-        row2key = {row: key for key, row in self.key2row.items()}
-        keys = xp.asarray([row2key[row] for row in rows],
-                           dtype='uint64')
-        return keys
+    def find(self, *, key=None, keys=None, row=None, rows=None):
+        '''Lookup one or more keys by row, or vice versa.
 
-    def get_rows(self, keys):
+        key (unicode / int): Find the row that the given key points to.
+            Returns int, -1 if missing.
+        keys (sequence): Find rows that the keys point to.
+            Returns ndarray.
+        row (int): Find the first key that point to the row.
+            Returns int.
+        rows (sequence): Find the first keys that points to the rows.
+            Returns ndarray.
+        '''
+        if sum(arg is None for arg in (key, keys, row, rows)) != 3:
+            raise ValueError("One (and only one) keyword arg must be set.")
         xp = get_array_module(self.data)
-        k2r = self.key2row
-        return xp.asarray([k2r.get(key, -1) for key in keys], dtype='i')
+        if key is not None:
+            if isinstance(key, basestring_):
+                key = hash_string(key)
+            return self.key2row.get(key, -1)
+        elif keys is not None:
+            keys = [hash_string(key) if isinstance(key, basestring_) else key
+                    for key in keys]
+            rows = [self.key2row.get(key, -1.) for key in keys]
+            return xp.asarray(rows, dtype='i')
+        else:
+            targets = set()
+            if row is not None:
+                targets.add(row)
+            else:
+                targets.update(rows)
+            results = []
+            for key, row in self.key2row.items():
+                if row in targets:
+                    results.append(key)
+                    targets.remove(row)
+            return xp.asarray(results, dtype='uint64')
 
     def add(self, key, *, vector=None, row=None):
         """Add a key to the table. Keys can be mapped to an existing vector
@@ -204,6 +229,8 @@ cdef class Vectors:
         row (int / None): The row number of a vector to map the key to.
         RETURNS (int): The row the vector was added to.
         """
+        if isinstance(key, basestring):
+            key = hash_string(key)
         if row is None and key in self.key2row:
             row = self.key2row[key]
         elif row is None:
@@ -248,7 +275,7 @@ cdef class Vectors:
             sims = xp.dot(batch, vectors.T)
             best_rows[i:i+batch_size] = sims.argmax(axis=1)
             scores[i:i+batch_size] = sims.max(axis=1)
-        keys = self.get_keys(best_rows)
+        keys = self.find(rows=best_rows)
         return (keys, best_rows, scores)
 
     def from_glove(self, path):
diff --git a/spacy/vocab.pyx b/spacy/vocab.pyx
index 14b62a808..675e4a805 100644
--- a/spacy/vocab.pyx
+++ b/spacy/vocab.pyx
@@ -286,14 +286,13 @@ cdef class Vocab:
         priority.sort()
         indices = xp.asarray([i for (prob, i, key) in priority], dtype='i')
         keys = xp.asarray([key for (prob, i, key) in priority], dtype='uint64')
-        
+
         keep = xp.ascontiguousarray(self.vectors.data[indices[:nr_row]])
         toss = xp.ascontiguousarray(self.vectors.data[indices[nr_row:]])
 
         self.vectors = Vectors(data=keep, keys=keys)
 
-        syn_keys, syn_rows, scores = self.vectors.most_similar(toss,
-                                        return_rows=True, return_scores=True)
+        syn_keys, syn_rows, scores = self.vectors.most_similar(toss)
 
         remap = {}
         for i, key in enumerate(keys[nr_row:]):