Most generative AI methods produce tens of thousands of unique and diverse high-scoring compounds when used without stringent filters. This is related to Brenner's underdetermined inverse problem stating that available data does not uniquely specify systems. Also, although there may be nothing chemically wrong with AI generated molecules (i.e., all atoms in common valences and charge states), some can be exotic, and an experienced medicinal chemist would reject them upfront. The issue of such unwanted molecules is manageable from a technical perspective. For example, one can enforce substructure rules and penalize the existence of undesired moieties (e.g., radicals, peroxides, anhydrides, strained and chemically unstable systems) in the reward functions, or as post-filters.