Furthermore, we examine the performance of achieving higher P(active) and QED upon the input molecules for a secondary analysis. Similar trend can be found that most RL settings perform better than No RL for all input compounds except compound 4 (Figure ). The reason why RL struggles with compound 4 might be that the predicted activity for the starting molecule is already very high, which makes it difficult to identify even more potent compounds with RL. Additionally, for compound 2 and compound 4, RL DF(scaffold) performs worse than RL noDF and RL DF(cmp) which indicates changes in the scaffold for these compounds does not improve activity and/or QED. One possible explanation for this is that the scoring function is not set and optimized towards improving predicted activity and QED explicitly. It aims to generate molecules with high scores, but not necessarily higher than the input molecules. This might also contribute to the observed high standard deviation across different runs. Overall, depending on the starting molecules' properties and structural complexity, it is not unexpected to observe different behaviors. For example, for compound 1 with P(active)=0.61, it appears to be easier to improve when exploring diverse scaffolds. While for compound 4 which already has P(active)=0.94, it is difficult to improve and change the scaffold.