Here, we combine our generative AI's ability to solve inverse design problems with the vast, readily synthesizable chemical universe offered by CSLs to rapidly identify compounds with desired properties and assured time-and cost-effective synthesis. We previously developed the Combinatorial Synthesis Library Variational Auto-Encoder or CSLVAE , a molecular graph-based generative AI that represents massive CSLs as differentiable, hierarchically-organized neural databases . By design, CSLVAE queries are contained in the CSL. Thus, CSLVAE generates molecular graphs, compounds, that are synthetically and cost-effectively accessible, with short lead times.