We conclude that transfer learning with a structurally diverse transfer learning set allows to generate structurally diverse molecules, comprising a broad range of scaffolds and possessing properties of the target space, e.g., an enriched fraction of sp 3 hybridized carbon atoms. This approach could help enrich screening compound collections with innovative compounds and scaffolds for virtual and real high-throughput screening.