Another relationship of interest is the trade-off between the amount of data used for model training (and the associated costs) and the goodness of the posterior rank predictions, which is displayed in Figure , and ordered by the total number of structures used for training. We observe the expected general tendency of the quality of posterior rank predictions to increase with the training set size, and that the improvement becomes less steep as more structures are used for model training. The results clearly demonstrate that the distribution of the training structures across the two levels is important in producing higher rank correlations at low computational cost. As an example, the distribution of sampled posterior rank correlations produced by the least expensive model providing the CRPS value below 1 kJ mol -1 (trained on 40%/10% fractions, as listed in Table ) performs considerably better than the model trained on 30%/30% fractions, at less than half of the cost.