Analogously, the same problem can be brought to generative models. A model can generate molecules that are outside of the target chemical space (false positives) or the output domain can collapse, and the model not be able to generate a chunk of the expected space (false negatives). This is very easy to assess when training models that try to generate the GDB-13 chemical space: any molecule outside of GDB-13 sampled is a false positive (closedness) and any other not being generated because of overfitting or limitations of the model is a false negative (completeness). When canonical and randomized SMILES models are compared, the results show that randomized SMILES models perform substantially better in both properties (Table ). They are able to learn better what limitations GDB-13 has and thus prevent the generation of incorrect molecules and at the same time generate more difficult outliers that comply with GDB-13 (Suppl. Figure , left tip of the NLL similarity maps).