In pre-clinical drug discovery applications, generative AI can design compounds that satisfy desired properties , for example bioactivity towards a target or physicochemical properties . Instead of explicitly enumerating and scoring molecular hypotheses as is common with predictive modeling, generative AI designs those hypotheses, solving the inverse problem . However, it is a major and widely recognized challenge to bias generative AI towards synthetic accessibility , despite efforts to include explicit synthetic accessibility cost functions , limit the model to fragments from known compounds , or induce bias towards simple and known synthetic pathways . Synthesis of the proposed compounds is therefore often time-and cost-intensive, hindering experimental validation and adoption in drug discovery .