The msFeaST workflow separates visual design considerations from data subdivision and clustering criteria. Clustering is dealt with separately, using its own optimality criteria. It thus allows for a more principled approach to subdividing spectral data that is not impacted by network visualization settings aimed at limiting visual overload (such as edge thresholds, top-K edge limits on each node, maximum cluster sizes). This separation is made possible by making use of its network visualization approach that remains manageable via "interactive details on demand". Here, interactive local topological neighborhood explorations guided by statistical information integration provide a robust means of exploring and visualizing data that can adapt to different similarity scores without extensive visual tuning. The msFeaST workflow thus seamlessly combines formal spectral similarity-based feature clustering, feature cluster prioritization using statistical contrast information, and local topological neighborhood exploration in a molecular networking like context. We believe that msFeaST can assist researchers in better understanding their untargeted metabolomics data and identifying relevant chemistry by leveraging both qualitative spectral and quantitative relative intensity information.