On the other hand, regarding aSNN we could obtain another perspective. The comparison of aSNN results for low/middle/high/utra-high activity compounds of FL and RL in public dataset were as follows; all of 5,000 generated against middle stage compounds: , Figure , Figure ). We see that for in-house datasets, the aSNN to the high/ultra-high activity compounds were mostly similar or less than that to low/middle activity compounds for both FL and RL. In the worst scenario, RL strategy failed to improve over FL such as the case of aSNN in top 100 and 500 generated compounds against the late-stage compounds (Figure ). Hence, we conclude that for public dataset RL strategy can work well; however, for inhouse dataset it has no benefit over FL in this evaluation. However, as discussed in the previous section the classification model accuracy may affect RL strategy (notably late-stage highactivity compounds in in-house projects B, D and F result in low predicted activity scores, Figure ), influencing whether the generative model can highly active compounds or not.