We next investigated whether variations of the protocol, namely the use of a diversity filter and inception, were able to improve performance metrics. Rediscovery obtained when using those options is shown in Table . Compared with the result of RL, we could not find any beneficial effect of a DF and inception when evaluating rediscovery. The aSNN results across model options in the middle stage are shown in Figure and those for late stage are shown in Figure . We also could not find any effect of DF and inception which contribute to an increase in the similarity of generated compounds to high or ultra-high activity compounds when evaluating aSNN. However, especially for DF, this option surely contributed to avoidance of mode collapse when evaluating uniqueness and novelty (Table and) . This can be seen by the increased variance in the learning curve in Figure where the RL-DF model must consistently identify new areas of chemical space that maximize the scoring function, compared with that of RF in Figure . Additionally, we investigated aSNN between the generated compounds from RL with/without DF and high/ultra-high activity compounds at the late stage for every 50 step. The result is shown in Figure where it can be seen that RL-DF kept low aSNN even when aSNN of RL increased according to the steps. So, the effect of DF should be thought as fitting for purpose, such as scaffold hopping. Regarding inception, it has also shown before that it could contribute to the lead optimization process ; however, in the current study there is no beneficial effect observed.