diff --git a/hippynn/layers/pairs/dispatch.py b/hippynn/layers/pairs/dispatch.py
index ea5a5f43..d4cce4a5 100644
--- a/hippynn/layers/pairs/dispatch.py
+++ b/hippynn/layers/pairs/dispatch.py
@@ -138,7 +138,7 @@ def neighbor_list_torch(cutoff: float, coords, cell):
 def neighbor_list_kdtree(cutoff, coords, cell):
     '''
     Use KD Tree implementation from scipy.spatial to find pairs under periodic boundary conditions 
-    with an orthonormal cell.
+    with an orthorhombic cell.
     '''
     
     # Verify that cell is orthorhombic
@@ -180,10 +180,16 @@ def neighbor_list_kdtree(cutoff, coords, cell):
     pairs = torch.as_tensor(pairs, device=coords.device)
     pair_first, pair_second = torch.unbind(pairs, dim=1)
 
+    # Wrap coordinates into cell and keep track of how they were translated
+    inv_cell = torch.linalg.inv(cell)
+    coords, wrapped_offset = wrap_points_torch(coords, cell, inv_cell)
+
     # Find difference vector between pairs without considering the MIC
     pair_diff = torch.sub(coords[pair_first], coords[pair_second])
 
     # Possible adjacent offset directions for images of the difference vector
+    # More is not needed because of the restriction that the cutoff is less than the length of 
+    # each side of the cell
     offset_range = torch.tensor(list(product([-1, 0, 1], repeat=3)), device=coords.device)
 
     # All adjacent offsets
@@ -198,8 +204,9 @@ def neighbor_list_kdtree(cutoff, coords, cell):
     # Index of shortest offset image
     pair_diff = torch.argmin(pair_diff, dim=1)
 
-    # Offset direction corresponding to shortest offset image
+    # Offset direction corresponding to shortest offset image plus accounting for the wrapping done earlier
     pair_image = offset_range[pair_diff]
+    pair_image -= (wrapped_offset[pair_first] - wrapped_offset[pair_second])
 
     # KDTree only returns each pair once (eg. (1,2) but not (2,1))
     doubled_pair_first = torch.concat((pair_first, pair_second))