Most data sets in chemical science are often considerably smaller than 150k with many as small as 500-5000 molecules, so we investigate the effect on training set size on the conclusions drawn thus far. Figure shows that the average overlaps are essentially converged for 5k molecules. The drop in average overlap on going to smaller training sets is more pronounced for the ground truth vector compared to the FPA vector but even for a training set of 100 molecules the average overlaps (0.44 and 0.70) are still significantly larger than for the null-model (0.31 and 0.58). Figures and show heat maps for molecules 1-4 for training set sizes of 100 and 1000 molecules, respectively. Comparison to Figure shows that for molecule 1 there is very little change in the heatmap, overlaps, and predicted logP value on going to the smaller training set. For molecule 2 the heatmap for 100, 1000, and 150k all look different from the ground truth and from each other while the error in the predicted logP is consistently high. In contrast, for molecule 3 the FPA overlap decreases from 0.77 to 0.70 to 0.33 on going from training set sizes of 100 to 1000 to 150K, while the error in the predicted logP value decreases from 1.91 to 0.58 to -0.01. The furan ring is correctly predicted to make a positive contribution to the logP for the two smaller training set sizes and the heatmaps are consistent with a positive logP value. More generally, the number of molecules with "sign problems" is lower for the smaller training set sizes as shown in Figure . Finally, for molecule 4 the main difference in the heatmaps is the growing contribution of some of the C atoms in the pyrimidine ring on going to larger training set sizes, but the FPA overlap is essentially unchanged.