The performance of all machine learning methods is dominated by their training set , so one of the most important questions for CoPriNet is to establish how well it generalises across different compounds. The specific challenge is that we can only obtain prices for the tiny fraction of the chemical space that is contained in the Mcule catalogue, and that prices for commercial catalogues are not generally in the public domain. We first tested that predictions are consistent independently of the random train/validation split. To do so, we trained CoPriNet on three distinct train/validation partitions, and found a high consistency, with mean PCC and SRCC of 0.73 and 0.74 with a standard deviation of 0.04 for PCC and 0.07 for SRCC. Next, we tested generalisability by analysing a set of noncatalogue compounds, namely non-purchasable natural products (NPNP), that are both quite different from the training/validation set (see Supplementary Information Figure for dataset comparison), and which can be reasonably assumed to be in general more expensive than purchasable compounds (PC), as they are much more difficult to synthesize. Figure shows that CoPriNet tends to predict larger prices for the NPNP compounds than for the compounds of the PC dataset, suggesting that it generalises well beyond its training set. In addition, we also studied CoPriNet performance using as test data the subset of compounds that were not present in the database snapshot used for training (March) but that were included in the next release (June). CoPriNet predictions exhibit a PCC of 0.65 (see Supplementary Information Figure ), far better than any SA score. Although it is true that the PCC value obtained on the default test set is better, it is important to notice that prices fluctuate over time, thus affecting the performance of our method. Fortunately, this limitation could be easily addressed by retraining the model after each database release.