diff --git a/ann_benchmarks.py b/ann_benchmarks.py
index 217178e6a..1402bab6c 100644
--- a/ann_benchmarks.py
+++ b/ann_benchmarks.py
@@ -22,7 +22,7 @@
 class BaseANN(object):
     pass
 
-        
+
 class LSHF(BaseANN):
     def __init__(self, metric, n_estimators=10, n_candidates=50):
         self.name = 'LSHF(n_est=%d, n_cand=%d)' % (n_estimators, n_candidates)
@@ -118,7 +118,7 @@ def __init__(self, metric, n_trees, n_candidates):
         self._n_trees = n_trees
         self._n_candidates = n_candidates
         self._metric = metric
-        self.name = 'PANNS(n_trees=%d, n_cand=%d)' % (n_trees, n_candidates)        
+        self.name = 'PANNS(n_trees=%d, n_cand=%d)' % (n_trees, n_candidates)
 
     def fit(self, X):
         self._panns = panns.PannsIndex(X.shape[1], metric=self._metric)
@@ -175,17 +175,40 @@ def query(self, v, n):
 
 
 class BruteForce(BaseANN):
-    def __init__(self, metric):
+    """kNN search that uses a linear scan = brute force."""
+    def __init__(self, metric, precision=numpy.float32):
+        if metric not in ('angular', 'euclidean'):
+            raise NotImplementedError("BruteForce doesn't support metric %s" % metric)
         self._metric = metric
+        self._precision = precision
         self.name = 'BruteForce()'
 
     def fit(self, X):
-        metric = {'angular': 'cosine', 'euclidean': 'l2'}[self._metric]
-        self._nbrs = sklearn.neighbors.NearestNeighbors(algorithm='brute', metric=metric)
-        self._nbrs.fit(X)
+        """Initialize the search index."""
+        lens = (X ** 2).sum(-1)  # precompute (squared) length of each vector
+        if self._metric == 'angular':
+            X /= numpy.sqrt(lens)[..., numpy.newaxis]  # normalize index vectors to unit length
+            self.index = numpy.ascontiguousarray(X, dtype=self._precision)
+        elif self._metric == 'euclidean':
+            self.index = numpy.ascontiguousarray(X, dtype=self._precision)
+            self.lengths = numpy.ascontiguousarray(lens, dtype=self._precision)
+        else:
+            assert False, "invalid metric"  # shouldn't get past the constructor!
 
     def query(self, v, n):
-        return list(self._nbrs.kneighbors(v, return_distance=False, n_neighbors=n)[0])
+        """Find indices of `n` most similar vectors from the index to query vector `v`."""
+        v = numpy.ascontiguousarray(v, dtype=self._precision)  # use same precision for query as for index
+        # HACK we ignore query length as that's a constant not affecting the final ordering
+        if self._metric == 'angular':
+            # argmax_a cossim(a, b) = argmax_a dot(a, b) / |a||b| = argmin_a -dot(a, b)
+            dists = -numpy.dot(self.index, v)
+        elif self._metric == 'euclidean':
+            # argmin_a (a - b)^2 = argmin_a a^2 - 2ab + b^2 = argmin_a a^2 - 2ab
+            dists = self.lengths - 2 * numpy.dot(self.index, v)
+        else:
+            assert False, "invalid metric"  # shouldn't get past the constructor!
+        indices = numpy.argpartition(dists, n)[:n]  # partition-sort by distance, get `n` closest
+        return sorted(indices, key=lambda index: dists[index])  # sort `n` closest into correct order
 
 
 def get_dataset(which='glove', limit=-1):
@@ -246,7 +269,8 @@ def get_queries(args):
             print len(queries), '...'
 
     return queries
-            
+
+
 def get_algos(m):
     return {
         'lshf': [LSHF(m, 5, 10), LSHF(m, 5, 20), LSHF(m, 10, 20), LSHF(m, 10, 50), LSHF(m, 20, 100)],
@@ -305,7 +329,7 @@ def get_fn(base, args):
     if os.path.exists(results_fn):
         for line in open(results_fn):
             algos_already_ran.add(line.strip().split('\t')[1])
-            
+
     algos = get_algos(args.distance)
     algos_flat = []
 
@@ -313,7 +337,7 @@ def get_fn(base, args):
         for algo in algos[library]:
             if algo.name not in algos_already_ran:
                 algos_flat.append((library, algo))
-                
+
     random.shuffle(algos_flat)
 
     print 'order:', algos_flat
diff --git a/install.sh b/install.sh
index 59068bcb5..9dbffd901 100644
--- a/install.sh
+++ b/install.sh
@@ -1,6 +1,6 @@
 sudo apt-get install -y python-numpy python-scipy
 cd install
-for fn in annoy.sh panns.sh nearpy.sh sklearn.sh flann.sh kgraph.sh glove.sh sift.sh
+for fn in bruteforce.sh annoy.sh panns.sh nearpy.sh sklearn.sh flann.sh kgraph.sh glove.sh sift.sh
 do
     source $fn
 done
diff --git a/install/bruteforce.sh b/install/bruteforce.sh
new file mode 100644
index 000000000..19c649d30
--- /dev/null
+++ b/install/bruteforce.sh
@@ -0,0 +1,3 @@
+sudo apt-get install -y python-pip python-dev
+sudo apt-get install -y libatlas-dev libatlas3gf-base
+sudo apt-get install -y python-numpy