Untargeted metabolomics deals with the comprehensive characterization of the composition of small chemicals, or metabolites, in biological samples. Typically, high-resolution LC-MS/MS (Liquid Chromatography Tandem Mass Spectrometry) workflows are used to provide comprehensive snapshots of the metabolome . However, despite recent advances in computational metabolomics, the reliable annotation of ms/ms spectral data remains a challenge . Hence, further complementary analyses, laborious manual annotations, and detailed validations of putative structure hypotheses remain a necessity . The exploratory analysis of typical untargeted metabolomics data is a daunting task due to large data heterogeneity and largely unknown chemical spaces. Here, molecular networking has established itself as a vital tool enabling researchers to organize and prioritize features for in-depth evaluations . Molecular Networking comprises a combination of data subdivision and exploratory data visualization. Large heterogeneous datasets are subdivided into smaller, more manageable feature groups based on spectral similarity scoring . Those feature groups are further presented to the user as subnetworks (molecular families) for exploration of relationships across features within them . While advantageous as a first analysis step, relationships between subnetworks are lost completely , parameter setting and optimization are time-consuming and opaque, and further customization of resulting molecular networks in Cytoscape is usually necessary to highlight statistical features for sub-network for feature prioritization .