Discovery proteomics experiments generate large amounts of data, but there is an unmet need for tools and workflows capable of extracting useful biological signals in complex multi-condition proteomics experiments, and particularly for proposing a small number of key targets to prioritise for follow-up experiments. We present a useful workflow for characterizing proteomics experiments that incorporate many conditions and abundance data, which incorporates the popular approach weighted gene correlation network analysis (WGCNA) and functional enrichment analysis with the PloGO2 R package. In this workflow we have extended the PloGO2 R package and made it available to Bioconductor, the open source repository of R software packages for the analysis of high-throughput omics data. The approach can use quantitative data from labelled or label-free experiments, and was designed and developed to handle multiple files stemming from data partition or multiple pairwise comparisons. Enrichment analysis will identify clusters or subsets of proteins of interest, and the WGCNA network topology scores will produce a ranking of proteins within these clusters or subsets. This can naturally lead to prioritized proteins to be considered for further analysis or as candidates of interest for validation in the context of complex experiments. We demonstrate our approach and the application of the workflow on two previously published datasets. In both, the automated workflow recapitulates key insights or observations of the published papers, and provides additional suggestions for further investigation. These findings indicate that dataset analysis using WGCNA combined with the updated PloGO2 package is a powerful method to gain biological insights from complex multi-condition proteomics experiments and provide information that may guide future investigations.