Small molecules can selectively modulate biological processes and thus generate phenotypic variation. Biological samples are complex matrices, and liquid chromatography tandem mass spectrometry often detects hundreds of molecules, of which only a fraction may be associated with this variation. The challenge therefore lies in the prioritization of the most relevant molecules for further investigation. Tools are needed to effectively contextualize mass spectrometric data with phenotypical and environmental (meta)data. To accelerate this task, we developed FERMO, a dashboard application combining mass spectrometry data with qualitative and quantitative biological observations. FERMO’s centralized interface enables users to rapidly inspect data, formulate hypotheses, and prioritize molecules of interest. We demonstrate the applicability of FERMO in a case study on antibiotic activity of bacterial extracts, where we successfully prioritized the bioactive molecule siomycin out of 143 molecular features. We expect that besides natural product discovery, FERMO will find application in a wide range of omics-driven fields.