We stress that the data demand is highly dependent on the nature of the data and the employed machine learning approach (including the feature representation), and there are distinct limits to generalizing our findings. Instead of a postmortem analysis of the learning curve as provided here, we will use an on-the-fly assessment of the learning curve combined with a just-in-time termination of the training data generation to minimize our data footprint in future studies. When considering the screening results, we are not only in a position to assess a large number of compounds, but we can also learn patterns from the data set in its entirety. Our analysis in Fig. shows the average density values and distributions of all compounds containing a given building block (cf. Fig. ). On the high density end, we find sulfur-heterocyclic moieties; the nitrogen-and oxygen-heterocycles yield medium density systems; and the low-density regime is dominated by carbon-based, non-heteroatomic building blocks. Molecules with B7 (1,3,4-thiadiazole) and B12 (1,2,5-thiadiazole) have the highest average densities, while those that incorporate B1 (CH 2 -linker) and B9 (cyclopentane) exhibit the lowest values. Aside from the linker groups, there is a clear correlation between density value and the heteroatom type and fraction in a corresponding moiety. Based on the construction of our library, more than 80% of the candidate compounds contain sulfur and more than 90% contain nitrogen. Fig. demonstrates how the density values depend on the weight percentage of the sulfur and nitrogen atoms in the compounds at hand. Our library thus yields the highest density values for molecules that by weight contain 30 to 50% sulfur and 20 to 30% nitrogen.