We added a naïve AL component into the RL loop based on the VS-AL model (Reinforcement Learning with Active Learning in Methods). In contrast to the immediate, rapid increase in hit rate observed in VS-AL, the RL-AL approach barely improves the hit rate obtained in the early phase and lead to moderate increases in total productivity by 30 000 epoch calls, resulting in 134±23 and 49±5 hits (48±7 and 47±5 scaffolds) and an oracle call efficiency of 0.16% for both ROCs and docking respectively. While an increase over the basic RL case, this is far from realizing the benefits observed in the VS-AL.