Despite the use of generative AI, this workflow still used human expertise to filter and select molecules for testing, resulting in level 1 automated chemical design . Challenges remain in fully automating this workflow and removing human influence while maintaining its success, challenges that are not unique to our approach. We note that visual inspection and novelty analysis for the selection of compounds for testing does not differ greatly between virtual screening and de novo approaches, however, the level of enrichment of A 2A chemistry beyond vendor libraries we have shown means that more success is to be found within the top ranked compounds. One key improvement would be more accurate scoring functions that go beyond providing enrichment to providing reliable, precise predictions for binding affinity and other important molecular properties at reasonable computational expense. As RL is a decision-making algorithm to optimize an arbitrary reward, the resulting decisions (i.e. molecular structures) are only as good as the arbitrary reward is descriptive of ground truth desirability.