Finally, heatmaps (Figure ) summarizing the distribution of the evaluation criterion as a function of two variables are generated for every pair of parameters for which multiple values were attempted. The value of each 2D coordinate in the heatmap corresponds to the maximum criterion value obtained across all parameterizations that used the combination of parameter values indicated by the coordinate; for example, a heatmap may show the behavior of the normalized LogAUC as a function of the electrostatic spheres (thin layer) and the desolvation spheres (thin boundary). Returning to the examples of HIVPR and FA7, we observe that HIVPR shows consistently low enrichment across candidate models, irrespective of how the parameters are varied, while FA7 shows a strong dependency on these parameters, thereby indicating the viability of pinpointing an optimal or near-optimal parameter combination for at least some targets. Examining the heatmaps in HTML reports for a pair of consecutive steps exemplifies the ability of the beam search algorithm to effectively "zoom in" on regions of parameter space that seem more promising (Figure ).