To understand the behaviour of molecules in a given environment is necessary as some of their properties depend on the conformational states they adopt (e.g., lipophilicity , passive membrane permeability , or dipole moment ). Although the conformational space is continuous, for the purposes of any kind of analysis it is often discretized by identifying a number of representative conformers, each of which is a substitute for a (potentially large) number of nearby conformers. This discretization requires the selection of a distance or similarity metric between conformers, which may be based solely on the geometry of a conformer (e.g., heavy-atom root-mean-squared deviation (RMSD)), a torsion based measure (e.g., torsion-fingerprint deviation (TFD) ), or further informed by additional factors like energy (e.g., as used in free-energy or energy-based clustering ). With this discretization of the conformational space, the minimum number of conformers needed to describe the ensemble can in principle be deduced. Following this logic, the size of the conformational space of a molecule, and thus its molecular flexibility, can be estimated by generating a large ensemble of conformers and then pruning them based on a chosen distance metric.