CSNs transform a multidimensional chemical space into a graph with the nodes representing chemical compounds and edges connecting compounds within a specific similarity boundary. These graphs provide immediate visualization that can be easily interpreted. CSNs can also be adequately characterized and compared using generally applicable statistical measures from network science. However, visualization becomes increasingly difficult as the number of compounds increases. Therefore, this method is not directly designed for diversity analysis. Recently, networks have been used as the basis for developing chemical library networks (CLNs) that can be used to explore the diversity of large and ultralarge molecular libraries. In general, representations of a tree-like nature, such as Tree MAP (TMAP), are more suitable for analyzing and interpreting large datasets .