First, we analysed the five most frequent scaffold and compared them the five most frequent scaffolds in the respective fine-tuning sets (Fig. [PPARd], and Supp. Fig. [PIM1, JAK2]). In general, using more similar molecules (Fig. ) for fine-tuning leads to a better matching of the most frequent molecular scaffolds by the CLMs. In such high-similarity settings, most methods (except for self-training) have a similar or better ability to reproduce 'recurrent' scaffolds than SMILES enumeration (Fig. ). This observation suggests a better capability to learn the underlying structural features of the fine-tuning sets compared to SMILES enumeration. Random atom masking showed the best ability to reproduce frequent scaffolds across experiments for both high and low similarity datasets, followed by token deletion. Another desirable property when performing de novo design is the capacity to generate chemically diverse structures that go beyond the molecules used for training. To this end, we analysed the ability of each augmentation strategy to generate diverse and novel molecular scaffolds compared to the molecules used for training. Using all the molecules generated during the transfer learning experiments, we measured (a) scaffold diversity, i.e., the number of novel scaffolds within the sampled molecules, and (b) scaffold novelty, i.e., the number of sampled scaffolds that are not in the fine-tuning or pre-training sets. The values obtained without using augmentation were reported as a baseline.