Dereplication represents a key step for rapidly identifying known secondary metabolites in complex biological matrices. In this context, Liquid Chromatography coupled to High Resolution Mass Spectrometry (LC-HRMS) is increasingly used and, via untargeted data-dependent MS/MS experiments, massive amount of detailed information on the chemical composition of crude extracts can be generated. An efficient exploitation of such datasets requires automated data treatment and access to dedicated fragmentation databases. Various novel bioinformatics approaches such as Molecular Networking (MN) and in-silico fragmentation tools have emerged recently and provide new perspective for early metabolite identification in Natural Product (NP) research. Here we propose an innovative dereplication strategy based on the combination of MN with an extensive in-silico MS/MS fragmentation database of NPs. Using two case studies we demonstrate that this combined approach offers a powerful tool to navigate through the chemistry of complex NPs extracts, dereplicate metabolites and annotate analogues of database entries.