Feature-based pharmacophore modeling is a well-established concept to support early stage drug discovery, where large virtual databases are filtered for potential drug candidates. The concept is implemented in popular molecular modeling software, including Catalyst, Phase, and MOE. With these software tools we performed a comparative virtual screening campaign on HSP90 and FXIa, taken from the 'maximum unbiased validation' data set. Despite the straightforward concept that pharmacophores are based on, we observed an unexpectedly high degree of variation among the hit lists obtained. By harmonizing the pharmacophore feature definitions of the investigated approaches, the exclusion volume sphere settings, and the screening parameters, we have derived a rationale for the observed differences, providing insight on the strengths and weaknesses of these algorithms. Application of more than one of these software tools in parallel will result in a widened coverage of chemical space. This is not only rooted in the dissimilarity of feature definitions but also in different algorithmic search strategies.