The detection of biologically meaningful clusters in protein interaction networks is crucial in systems biology. Among its applications, it can enable the identification of protein complexes. Notwithstanding significant advances, the detection of meaningful clusters faces important challenges, including the need to aid researchers in the prioritization of hundreds or even thousands of clusters. To address this need, we developed a method for the prioritization of network clusters based on the analysis of their functional homogeneity, Horn. Based on Horn scores, clustering results can be statistically ranked and attention directed toward clusters that are more likely to be biologically meaningful. We tested it on a global human protein-protein interaction network and four network clustering algorithms. Our method substantially reduced the space of potentially spurious clusters. Furthermore, we evaluated its protein complex detection capability on an independent reference dataset of protein complexes. Irrespectively of clustering approach, our approach improved protein complex identification capacity.