In this work, we reviewed generative models for molecules in light of the current evaluation techniques. Our conclusion regarding distribution-learning is that extremely simple and practically useless models can get near perfect scores on many metrics currently in use. We observed that many of the best technologies according to benchmark studies ) are likelihood based models and we argue that future comparisons should include test set likelihoods. This would correspond to a comprehensive metric that could improve upon previously suggested measures which can be "tricked".