We developed a novel sample-centric and knowledge-driven computational metabolomics pipeline to explore large NPs extract datasets. This approach ensures an efficient integration of new samples over time while limiting the loss of valuable experimental data in hermetic project-related silos. The employed semantic web technologies facilitate the merging of experimental data with knowledge available from existing public scientific resources. We showcased the utility of this pipeline in a drug discovery context by exploring a 1,600 plant extract collection that was screened against trypanosomatids. Here, we could rapidly annotate known anti-T. cruzi compounds in an active extract, annotate unknown anti-T. cruzi compounds common to active extracts, and identify an active analog of reported anti-L. donovani compounds. These different applications demonstrate the flexibility of the developed framework and the possibility it offers to explore NPs metabolomics data in drug discovery context and beyond. Great advances still need to be made to democratize semantic web technologies in NPs