Metabolomics, a rapidly developing post-genomic approach, has proven to be a powerful and indispensable tool to interrogate cellular biochemistry, investigating metabolism and its reciprocal crosstalk with cellular signaling and regulation . The metabolic profiles may be seen as functional signatures of the physiological state of the biosystem under investigation, i.e., snapshots of partially-mapped molecular landscapes, comprising effects of genetic regulation, as well as environmental factors . However, the realization of a holistic coverage of the whole metabolome, in a given biological system, is still currently not feasible (at least with a single method) at the metabolite extraction  and analytical  levels. Furthermore, this current “unfeasibility” can be expanded to the handling of the data from untargeted metabolomics studies: how do we maximize the value of untargeted metabolomic data  with the current chemometric methods and algorithms?