Overall, for local molecular optimization, the goal is to generate (1) unique molecules that are (2) highly similar (i.e. Tanimoto similarity > 0.7) to the input molecule while also showing (3) desirable properties (improved properties in this case). Achieving all these criteria is important and challenging since they can be conflicting. The prior, scoring objectives and diversity filter have direct impact on similarity, desirable properties, and uniqueness respectively. Lowering σ brings the agent closer to the prior, thus producing more similar molecules but also finding less compounds with improved properties. Scoring objectives guide the agent towards the chemical space of high scoring compounds but this could also lead to devia-tions from the prior. The diversity filter helps in exploring more unique compounds but could also have less similar compounds when σ(=120) is high. Therefore, it is crucial to understand and consider the impact of these factors.