Figure shows the results for the molecular optimization task with molecules achieving higher P(active) and QED scores. Most RL settings perform better than No RL on all evaluation metrics except for compound 4. RL DF(cmp) generally generates more compounds with improved properties and are less similar to the input molecule, as can be seen from the lower number of compounds with Tanimoto similarity>0. generating molecules that are more similar to the input compound, which is useful for local molecular optimization -exploration of the close chemical space of an input compound.