Most RL settings perform better than No RL in terms of generating molecules with the predicted probability to be active>0.6 and QED>0.6 via all evaluation metrics and for all input compounds (Figure ). RL DF(cmp) generates more unique compounds than RL noDF, which validates the advantage of penalizing the compounds that have been generated frequently to improve diversity. RL DF(scaffold) generates more unique scaffolds and generic scaffolds than RL noDF and RL DF(cmp), which suggests the benefit of penalizing the frequent generated scaffolds. Especially for scaffold discovery efforts, this can be a useful strategy to increase scaffold diversity.