diff --git a/src/kd_tree.jl b/src/kd_tree.jl
index 5518d7d..afc869d 100644
--- a/src/kd_tree.jl
+++ b/src/kd_tree.jl
@@ -226,18 +226,24 @@ function inrange_kernel!(tree::KDTree,
         return 0
     end
 
+    M = tree.metric
+
     # At a leaf node. Go through all points in node and add those in range
     if isleaf(tree.tree_data.n_internal_nodes, index)
         return add_points_inrange!(idx_in_ball, tree, index, point, r)
     end
 
+    max_dist = get_max_distance_no_end(M, hyper_rec, point)
+    if max_dist < r
+        return addall(tree, index, idx_in_ball)
+    end
+
     split_val = tree.split_vals[index]
     split_dim = tree.split_dims[index]
     lo = hyper_rec.mins[split_dim]
     hi = hyper_rec.maxes[split_dim]
     p_dim = point[split_dim]
     split_diff = p_dim - split_val
-    M = tree.metric
 
     count = 0
 
@@ -257,11 +263,6 @@ function inrange_kernel!(tree::KDTree,
     # Call closer sub tree
     count += inrange_kernel!(tree, close, point, r, idx_in_ball, hyper_rec_close, min_dist)
 
-    # TODO: We could potentially also keep track of the max distance
-    # between the point and the hyper rectangle and add the whole sub tree
-    # in case of the max distance being <= r similarly to the BallTree inrange method.
-    # It would be interesting to benchmark this on some different data sets.
-
     # Call further sub tree with the new min distance
     split_diff_pow = eval_pow(M, split_diff)
     ddiff_pow = eval_pow(M, ddiff)