Another challenge is the subsequent analysis of the large volume of results. Due to the well-known deficiencies in docking scoring functions, visual inspection of docked poses plays an important role in the selection of compounds for further investigation. However, the number of molecules that can be visually inspected is many orders of magnitude lower than the number of docked poses, making it crucial to have means to prioritize hits for visual inspection. Often, the prioritization criteria involve filtering the results by desirable or undesirable interactions between ligands and receptors, which may compensate for the shortcomings of the scoring function in identifying true binders. This data processing is an important yet poorly-defined step in virtual screening pipelines, often performed in an iterative approach where different filtering criteria are devised and refined through multiple rounds of visual inspection. Despite its importance, performing this crucial data analysis in a rigorous way is difficult and often the tracking of steps and filter criteria is left as a burden to the user. For laboratories that routinely perform large virtual screenings and have the opportune know-how, it is common to implement in-house database solutions, which add further requirements in terms of development effort and maintenance.