diff --git a/spacy/tests/vocab_vectors/test_vectors.py b/spacy/tests/vocab_vectors/test_vectors.py
index 214285b01..b688ab9dd 100644
--- a/spacy/tests/vocab_vectors/test_vectors.py
+++ b/spacy/tests/vocab_vectors/test_vectors.py
@@ -141,7 +141,6 @@ def test_vectors_most_similar(most_similar_vectors_data):
     assert all(row[0] == i for i, row in enumerate(best_rows))
 
 
-@pytest.mark.xfail
 def test_vectors_most_similar_identical():
     """Test that most similar identical vectors are assigned a score of 1.0."""
     data = numpy.asarray([[4, 2, 2, 2], [4, 2, 2, 2], [1, 1, 1, 1]], dtype="f")
@@ -315,4 +314,4 @@ def test_vocab_prune_vectors():
     assert list(remap.keys()) == ["kitten"]
     neighbour, similarity = list(remap.values())[0]
     assert neighbour == "cat", remap
-    assert_allclose(similarity, cosine(data[0], data[2]), atol=1e-6)
+    assert_allclose(similarity, cosine(data[0], data[2]), atol=1e-4, rtol=1e-3)
diff --git a/spacy/vectors.pyx b/spacy/vectors.pyx
index 0f015521a..44dddb30c 100644
--- a/spacy/vectors.pyx
+++ b/spacy/vectors.pyx
@@ -344,8 +344,12 @@ cdef class Vectors:
                 sorted_index = xp.arange(scores.shape[0])[:,None][i:i+batch_size],xp.argsort(scores[i:i+batch_size], axis=1)[:,::-1]
                 scores[i:i+batch_size] = scores[sorted_index]
                 best_rows[i:i+batch_size] = best_rows[sorted_index]
-
+        
         xp = get_array_module(self.data)
+        # Round values really close to 1 or -1
+        scores = xp.around(scores, decimals=4, out=scores)
+        # Account for numerical error we want to return in range -1, 1
+        scores = xp.clip(scores, a_min=-1, a_max=1, out=scores)
         row2key = {row: key for key, row in self.key2row.items()}
         keys = xp.asarray(
             [[row2key[row] for row in best_rows[i] if row in row2key]