Finally, we tested generalizability with another set of molecules substantially different from those in the training set (see Supplementary Information Figure for dataset comparison), namely a set compiled from virtual compounds included in the Mcule catalogue. These are compounds likely to be easily synthesizable from accessible building blocks and for which prices are estimated by the providers according to expected synthetic routes and requirements; as a result, price distributions tend to be substantially different from the one of the training set. For these compounds, the correlations with price are poor for all SA scores CoPriNet predictions are also worse, as they systematically underestimate prices, leading to a poor linear correlation. This can be partially explained by vendor's differences in pricing strategies (see Supplementary Information Section 7) as well as by the fact that virtual compounds tend to include additional "on-demand" fees that could hide the actual synthesis cost. Even so, the ranking performance (SRCC 0.56) is far better than that of the best performing SA metric (SCScore, SRCC of 0.32). This is important because in practice, prioritisation is generally conducted by selecting the top most promising compounds, so that a reliable ranking is even more important than accurate price prediction. Therefore, it is likely that CoPriNet predictions will be useful even across catalogues.