The computational experiments aim to evaluate whether RL could improve the performance of transformerbased generative models in generating molecules with desired properties. The evaluation is conducted for two application scenarios, 1 Scaffold discovery: generate new scaffold ideas that are active against the dopamine receptor type 2 (DRD2) target. 2 Molecular optimization: generate close analogues to improve the activity against the DRD2 target compared to the input molecule. As a proxy for biologcal activity, we use the DRD2 activity model from Olivecrona et al. which was trained on data extracted from ExCAPE-DB . The output of the model is the predicted probability of a given molecule to be active (pIC50≥5). For both scaffold discovery and molecular optimization, it is common to start with compounds which have already shown reasonable potency. Four compounds were selected from the DRD2 active compounds with pIC50≥5 in ExCAPE-DB as input starting molecules for thorough investigation. Figure shows the input compounds, pIC50, the predicted probabilities to be active P(active) and Quantitative Estimate of Drug-likeness (QED) scores. These compounds were selected to simulate different challenges with respect to input starting structure and property score. Additionally, as a supplementary analysis, 100 compounds were selected from the DRD2 active compounds as input compounds, with each compound having P(active)>0.5 and being randomly chosen from the top 100 most frequent unique scaffolds.