In order to facilitate the curation of diverse and unbiased downsampled subsets of chemical libraries, we developed Dedenser. Available as a Python package, Dedenser is built around a downsampling algorithm which provides users with the ability to systematically reduce the number of molecules in a chemical library represented as a (chemical) point cloud. The downsampling process employed preserves the overall 3D topology of the chemical point cloud, and consequently the chemical space occupied by the set of molecules. This downsampling algorithm is largely defined as a two phase process: 1) the identification of clusters and singleton molecules and 2) volume/density based downsampling of clusters. For ease of use we have developed a command line interface for Dedenser, requiring no coding experience to utilize. Herein we discuss the details surrounding the theory, implementation, and brief case studies of Dedenser. Overall Dedenser provides an alternative to methods that draw single representative compounds from clusters, but instead provides diversity by downsampling the cluster such that an evenly distributed set of compounds is generated within each cluster based on relative cluster size.