The chemical space networks (CSNs), proposed and developed by Maggiora and Bajorath, start by measuring the pairwise similarity between the molecules in a data set (using a given similarity coefficient and a compound representation). Then, the molecules are represented by nodes which are connected if the similarity is larger than an established threshold. A limitation of this approach is that it is difficult to visualize networks for large compound data sets. Moreover, this approach also requires O(N 2 ) operations, so it is not well-suited to represent large sections of the chemical space. To overcome this issue, the eCSNs have been recently proposed (vide supra). This is a natural generalization of the CSNs, in which any given molecular set can be taken as a node in the network (in this study, the nodes will be the 11 libraries). Then, the relations between these nodes are established via the extended similarity calculated for the union of the corresponding libraries. This coarse-grained representation is markedly more efficient since it exploits the more favorable computational scaling of the n-ary indices.