Figure shows the resulting changes in metric values during fine-tuning where Epoch-0 represents the generative model before fine-tuning began. It is quickly possible to assess that some intrinsic properties (Figure ) like novelty and diversity decrease with increasing finetuning epochs, while validity has an initial drop that is recovered with further fine-tuning epochs as it adjusts to new chemotypes. Meanwhile, similarity to the initial pre-training dataset (ChEMBL compounds) decreases as shown by an increase in Fréchet ChemNet Distance and decrease in analogue coverage (Figure ). Note that metrics that measure the presence of only a single similar molecule, like analogue similarity and single nearest neighbour increase, as the initial ChEMBL training dataset will likely already contain A2A-like chemotypes. Conversely, similarity to the fine-tuning set of A2A ligands increases especially noticeable by analogue similarity and coverage (Figure ), while novelty also slowly decreases with respect to this fine-tuning set. This overview of property changes allows for interpretation on how many fine-tuning epochs are required. In this case, arguably, just one or two epochs are needed which quickly leads to an increased similarity to the fine-tuning set with marginal improvements with any further epochs; however, further epochs do lead to an undesirable decrease in novelty and diversity. The required balance will vary depending on user and use case, however, quickly assessing changes is always useful.