A growing concern in natural products, food and drug discovery research is the progressive loss of biodiversity and the extinction of unexplored species. This gradual extinction of many unexplored plants and other organisms represents the loss of many potentially new bioactive and valuable chemicals. Thus, there is a need for new approaches for accurate documentation of these compounds to allow an efficient cataloging of natural products. Mainly due to the wide range of structural diversity, phytochemistry has become a science focused on progressive fractionations to a pure compound to have its structure and biological activity elucidated. However, the idea of chance once associated with natural products discoveries is now outdated and this major field of research is becoming driven by the 'omics' technologies. Genomics, transcriptomics, proteomics, and metabolomics form a holistic approach to a better understanding of complex biological systems. The first three will not be addressed directly in here. Metabolomics, as the last of the 'omics' sciences, is the term used to describe an analytical approach that aims to identify and quantify all metabolites involved in a specific situation within a given organism or system. It uses analytical measurements and statistical techniques to deliver phenotypic results to be interpreted as the ultimate consequence of genome and proteome modulation. Once there is statistical consideration, a sizeable number of replicate samples must be analyzed to provide statistical confidence. Considering both natural products and metabolomics (in its wide application), one of the main bottlenecks is compound identification. The term "metabolomics dark matter" was coined to emphasize this critical fact that most compounds in a study are actually unidentified. When the selected biomarker is known and its spectrum is recorded in an available database, one can simply compare the analytical data and identify it. This is routinely done for biological samples, especially in studies involving metabolomics in humans (and other model organisms), where the wide range of compounds is well known and well recorded in various databases. This is not quite the same for natural products, where the chemical diversity is much wider, with varying physicochemical properties, and the available databases are not well-organized, comprehensive, or freely accessed in many cases. The complex biosynthesis of secondary metabolites leads to the possibility of finding many analog derivatives at different stages of biosynthesis, as well as its metabolized forms. In such cases, many of the diagnostic peaks will overlap, causing misunderstandings between multiple possible targets. In natural products, the term dereplication is defined as the approach that allows the identification of known compounds in the early stages of research, in order to avoid investments in the production of known (or replicated) results. This approach relies heavily on databases for matching and identifying chemical profiles in often complex samples. However, because databases are unable to represent all known compounds, we can redefine dereplication as the effort to identify known, well-cataloged compounds in early stages of the research, which is not enough.