This study highlights the versatility of generative deep learning for hit and lead finding in drug discovery, where the same computational pipeline can be used to both create new molecules and screen libraries of existing compounds. We envision future projects in which de novo design methods are first validated for physically available molecules from a compound repository or commercial suppliers before investing in potentially more expensive and time-consuming syntheses. This approach could help accelerate the design-make-test,-analyze cycles, and reduce the risk of failure .