We observe that FaST, which builds molecular graphs by combining fragments using RL, shows a higher success rate across all compared method. FaST and RationaleRL have an advantage in terms of diversity and novelty. Both are graph generation approaches that utilize a set of substructures as building blocks to compose molecular graphs. The substructures are extracted from known active molecules which means that both methods start from a good starting point taking advantage of prior knowledge. Furthermore, it should be noted that the scoring function that guides the optimization across all approaches is based on ML models and therefore the accuracy of the function depends on how far the generated molecules are from the dataset used to develop the ML models . Given that RationaleRL utilizes substructures extracted from the known inhibitors which have come from the dataset used to train the ML models, the scoring function is expected to be more accurate. On the other side, both REINVENT and MOLRL start from random molecules which could be far from what the ML classifiers have been trained on. Despite that, MOLRL achieves comparable novelty with RationaleRL and highest success rate.