Building on the general optimization results of the chemical language model, we investigated the potential of transfer learning to create novel and diverse virtual compound libraries for drug discovery. To enrich sets of generated molecules with features relevant for drug discovery , we applied transfer learning to navigate between two spaces: a synthetic compound space ("source space") of bioactive molecules compiled from ChEMBL24 and a chemical space of natural products ("target space") defined by natural products from the manually curated natural product screening library MEGx (Analyticon Discovery GmbH, Potsdam, Germany).