Multiple binding modes are commonly identified experimentally using X-ray crystallography and NMR techniques, which can be timeconsuming or costly. Information about multiple binding modes present in electron density maps is often overlooked during construction of the 3-dimensional structure during X-ray crys-tallography. One recent study showed that up to 29% of protein-ligand co-crystal structures deposited in the Protein Data Bank have unmodeled ligand conformations and sometimes even unrealistic single bound conformations that are based on averaged electron density contributions from multiple binding modes. The same study also found that less than 2% of ligand-bound crystal structures in the PDB database contained multiple ligand conformations. Additionally, crystal preparation methods like cryocooling can alter the binding modes. Thus, simply having a high-resolution crystal structure does not always guarantee adequate information for structure-guided design. On the other hand, binding affinity studies such as based on calorimetry present an overall picture of the protein-ligand interactions in solutions, but are hard to interpret in terms of contribution of each individual binding mode. Thus, there is a need for computational tools which can provide information about ligand conformational heterogeneity during rational structure-based drug design.