In a pipeline, where fragment hits are combined, analogues identified by catalogue, and then placed, the next challenge becomes choosing which compounds to purchase, a problem shared with other methodologies. Three operations are commonly performed: filtering, sorting, and clustering. One possible filter is supplier driven, namely the removal of compounds above a given price point or with unworkable delivery times. Another possible filter is the wholesale removal of compounds with substructures that may cause assay interference, such as fluorescence or PAINS, or may be toxic (e.g. Ghose or REOS filters), or may not be drug-like (eg. Lipinski rules) . Whereas sorting by predicted energy or similar score is the simplest approach, it is less suitable in the real world than a combination of different metrics in addition to score or number of interactions. One factor is risk, whereas a conservative elaboration may be more likely to bind, more information may be gained from a riskier derivative compound. A variety of other factors could be considered such as a penalty for rotatable bonds on account of entropic loss from the decrease in degrees of rotational freedom upon binding. One further step, especially useful for hit discovery, is clustering by the interactions formed. These various steps together better reflect a drug discovery campaign as they allow a set of virtual compounds with desired properties and diverse binding modalities to be shortlisted as opposed to simply by predicted energy.