The suite of performance metrics does not necessarily need to be run on a molscore output (for example, Figures ). Instead, it can be used to assess arbitrary datasets for quick comparison to reference datasets. For example, evaluating progress during generative model fine-tuning. In this case study, we use a SMILES-based RNN pre-trained on ChEMBL compounds and fine-tune it (via transfer learning) using a set of known Adenosine A2A receptor (from here on A2A) ligands to bias de novo molecule generation towards A2A-bioactive-like chemotypes. This just requires two lines of Python to instantiate the GetMetrics class specifying any reference datasets and calling calculate to calculate the metrics (in this case, repeated for sampled de novo molecules after each epoch of fine-tuning).