three RL settings, RL DF(cmp) Sim generates the most unique similar compounds while RL DF(cmp) generates the most unique compounds with improved properties. This is because without Tanimoto similarity included in the scoring objective, the agent could explore chemical space more freely in search of high scoring compounds, potentially deviating from the prior. In general, RL DF(cmp) Sim performs best in finding compounds with both similarity and improved properties. RL DF(cmp) generates more unique similar compounds than RL noDF as shown in Figure indicating the benefit of diversity filter to improve uniqueness. When σ=120, RL DF(cmp) mostly be-comes worse than RL noDF. This might be because the high σ shifts the agent away from the prior.