The primary objective of these conformation generation tools is to identify the global minima or a list of lowenergy conformers from a large ensemble of generated conformations. The accuracy, speed, and computational reliability of these tools are achieved through different algorithmic approaches. However, it is crucial to validate the results obtained from these tools with experimental findings. The validation of ligand conformations often involves comparing the generated conformers with experimentally determined structures, typically obtained from proteinbound ligand conformations extracted from the Protein Data Bank (PDB). A shortcoming of these so-called "bioactive conformers" for the validation of conformation generation software is the limited number and diversity of experimentally determined protein-ligand structures and questions surrounding whether these "bioactive" conformers represent the global minimum or local minimum or high energy structures in the conformational ensemble. Additionally, X-ray structures in the PDB represent static snapshots of molecules in crystalline states, which may not fully capture their dynamic behavior in solution or other environments. The resolution of X-ray structures is used as a quality criterion and low-resolution structures may lack precision and atomic-level details necessary for accurate conformation determination. It is crucial to consider these limitations and explore alternative validation approaches, such as benchmark datasets or comparison with other experimental data. In addition to the Protein Data Bank (PDB), another widely used validation dataset for ligand conformations is the Cambridge Structural Database (CSD). The CSD primarily consists of small organic molecule crystal structures obtained from X-ray crystallography experiments. It offers a large collection of experimentally determined structures, providing valuable insights into the three-dimensional arrangements and intermolecular interactions of small molecules in the solid state.