diff --git a/metric_learn/constraints.py b/metric_learn/constraints.py
index 89a3b48d..b71c9b96 100644
--- a/metric_learn/constraints.py
+++ b/metric_learn/constraints.py
@@ -79,6 +79,12 @@ def chunks(self, num_chunks=100, chunk_size=2, random_state=None):
     chunks = -np.ones_like(self.known_label_idx, dtype=int)
     uniq, lookup = np.unique(self.known_labels, return_inverse=True)
     all_inds = [set(np.where(lookup == c)[0]) for c in xrange(len(uniq))]
+    max_chunks = int(np.sum([len(s) // chunk_size for s in all_inds]))
+    if max_chunks < num_chunks:
+      raise ValueError(('Not enough possible chunks of %d elements in each'
+                        ' class to form expected %d chunks - maximum number'
+                        ' of chunks is %d'
+                        ) % (chunk_size, num_chunks, max_chunks))
     idx = 0
     while idx < num_chunks and all_inds:
       if len(all_inds) == 1:
@@ -93,9 +99,6 @@ def chunks(self, num_chunks=100, chunk_size=2, random_state=None):
       inds.difference_update(ii)
       chunks[ii] = idx
       idx += 1
-    if idx < num_chunks:
-      raise ValueError('Unable to make %d chunks of %d examples each' %
-                       (num_chunks, chunk_size))
     return chunks
 
 
diff --git a/test/test_constraints.py b/test/test_constraints.py
new file mode 100644
index 00000000..a135985e
--- /dev/null
+++ b/test/test_constraints.py
@@ -0,0 +1,63 @@
+import unittest
+import pytest
+import numpy as np
+from sklearn.utils import shuffle
+from metric_learn.constraints import Constraints
+
+SEED = 42
+
+
+def gen_labels_for_chunks(num_chunks, chunk_size,
+                          n_classes=10, n_unknown_labels=5):
+  """Generates num_chunks*chunk_size labels that split in num_chunks chunks,
+  that are homogeneous in the label."""
+  assert min(num_chunks, chunk_size) > 0
+  classes = shuffle(np.arange(n_classes), random_state=SEED)
+  n_per_class = chunk_size * (num_chunks // n_classes)
+  n_maj_class = chunk_size * num_chunks - n_per_class * (n_classes - 1)
+
+  first_labels = classes[0] * np.ones(n_maj_class, dtype=int)
+  remaining_labels = np.concatenate([k * np.ones(n_per_class, dtype=int)
+                                     for k in classes[1:]])
+  unknown_labels = -1 * np.ones(n_unknown_labels, dtype=int)
+
+  labels = np.concatenate([first_labels, remaining_labels, unknown_labels])
+  return shuffle(labels, random_state=SEED)
+
+
+@pytest.mark.parametrize("num_chunks, chunk_size", [(5, 10), (10, 50)])
+def test_exact_num_points_for_chunks(num_chunks, chunk_size):
+  """Checks that the chunk generation works well with just enough points."""
+  labels = gen_labels_for_chunks(num_chunks, chunk_size)
+
+  constraints = Constraints(labels)
+  chunks = constraints.chunks(num_chunks=num_chunks, chunk_size=chunk_size,
+                              random_state=SEED)
+
+  chunk_no, size_each_chunk = np.unique(chunks, return_counts=True)
+
+  np.testing.assert_array_equal(size_each_chunk, chunk_size)
+  assert chunk_no.shape[0] == num_chunks
+
+
+@pytest.mark.parametrize("num_chunks, chunk_size", [(5, 10), (10, 50)])
+def test_chunk_case_one_miss_point(num_chunks, chunk_size):
+  """Checks that the chunk generation breaks when one point is missing."""
+  labels = gen_labels_for_chunks(num_chunks, chunk_size)
+
+  assert len(labels) >= 1
+  constraints = Constraints(labels[1:])
+  with pytest.raises(ValueError) as e:
+    constraints.chunks(num_chunks=num_chunks, chunk_size=chunk_size,
+                       random_state=SEED)
+
+  expected_message = (('Not enough possible chunks of %d elements in each'
+                       ' class to form expected %d chunks - maximum number'
+                       ' of chunks is %d'
+                       ) % (chunk_size, num_chunks, num_chunks - 1))
+
+  assert str(e.value) == expected_message
+
+
+if __name__ == '__main__':
+  unittest.main()