Here, we examine the effect of RL in a larger scale, specifically with 100 input starting molecules. A single run is conducted for each configuration. Figure shows the results for the scaffold discovery task with each point representing the performance for each input starting molecule. Clearly, all three RL settings generate more unique compounds, scaffolds and generic scaffolds that show P(active)>0.6 and QED>0.6 than No RL, with RL DF(scaffold) performing the best followed by RL DF(cmp) and RL noDF. This re validates the advantage of RL over No RL, and the use of diversity filtering which penalizes frequently generated compounds or scaffolds to improve diversity. Figure shows the results for the molecular optimization task. All three RL settings generate more unique compounds that show improved P(active) and QED than No RL. However, for generating unique similar (i.e. Tanimito similarity > 0.7) molecules, RL noDF and RL DF(cmp) perform worse than No RL. RL DF(cmp) generates the most unique compounds with improved properties but least in the unique similar compounds. The reason could be that the agent was guided to focus on improving properties (enforced by scoring objectives) and unique molecules (enforced by diversity filter) which might deviate from the prior for generating similar molecules. RL DF(cmp) Sim which includes Tanimoto similarity as an additional scoring objective help generate more similar compounds as shown in Figure , and move towards the end goal of both improved properties and similarity in Figure .