Having demonstrated the ability of the chemical language model to generate scaffold-focused de novo sets, we explored the application of transfer learning to expand the sampled chemical space from the training space to the target space. Here, the transfer learning set contained molecule 1 and four dissimilar natural products (6-9, Fig. ) to increase the diversity of the fine-tuning set and observe its effect on the structure of the generated molecules. We observed that both FCD curves evolved continuously as a function of the number of epochs (Fig. ).