Therefore, we conducted a search of DFs and parameters to identify a more optimal configuration that would successfully and robustly rescue mode collapse (Experiment 3 in Figure ). Various DF parameters were tested against 3 example optimization objectives taken from the GuacaMol benchmark suite . The prior was therefore trained on the GuacaMol train set with and identical RNN configuration to the GuacaMol LSTM baseline model and trained for the same number of epochs . The tasks were optimized using AHC in combination with DF1 for 500 RL updates. The area under the training curve of three endpoints measured (uniqueness, goal/score and run time) are shown in Figures . In all cases, we found that too high a minimum score threshold (> 0.5) leads to poorer performance. For uniqueness, linear and sigmoid output modes performed best (and better with lower bin sizes) in the Aripiprazole and Osimertinib tasks. However, with respect to the objective, there was less discrepancy between output modes and the bin size relationship reversed (with higher bin sizes showing better performance). In these tasks, the identicalmurckoscaffold and scaffoldsimilarityecfp DFs outperformed the other DFs, while scaffoldsimilarityatompair seemed to result in an unusually long run time. Based on these results, as well as the rationale of softening the gradient of penalization, we decided to continue using scaffoldsimilarityecfp but lowered the minimum score threshold to 0.5, changed the output mode to linear and increased the bin size to 50. This configuration is from here on referred to as DF2.