Generative deep learning proved applicable to computer-based compound library design for use in medicinal chemistry. The results demonstrate that chemical language models combined with transfer learning support the discovery of new molecular architecture for drug design. Chemical language models proved able to navigate through chemical space using the SMILES molecular representation. By relying on the chemical similarity principle and natural products as starting points for drug design, this computational approach successfully generated novel and chemically diverse molecular entities. This pretrained chemical language model is publicly accessible to enable experimentation along with the analysis framework and an interactive map to encourage researchers to apply transfer learning on custom sets of molecules for own chemical space exploration. It should be noted that this computational framework does not explicitly assess the synthesizability of molecules, and further compound ranking and prioritization may be required. Keeping these constraints in mind, only broad prospective application of this machine learning model will reveal if the underlying data-driven approach has the potential to accelerate the identification of novel bioactive compounds for early-stage drug discovery.