For each molecule we add to G, we keep track of its original parent (the molecule from the 800 test molecules). After training, for each of the 800 test molecules, we take the set of translated molecules in G, and select the one with the highest property score. Results are shown in Table ; our method greatly outperforms the other baselines, but we point out a few flaws intrinsic to the task. Because the similarity is computed through Morgan fingerprint, which are hashes of substructures, repeatedly adding aromatic rings can often not change the fingerprint by a lot. Nevertheless, adding aromatic rings will linearly increase penalized logP score, which allows trivial solutions to produce high scores for this task (see Figure ). This phenomenon is noted by Nigam et al. ( ), but they add a regularizer to constrain the generated compounds to look similar to the reference molecules. Due to the mentioned issues, we believe this task can be reformulated. For instance, one could use a different fingerprint method so that the fingerprint similarity is not so easily exploited (see AP ( In general, the task of optimizing (increasing) the penalized logP scores is not entirely meaningful.