A huge amount of data has been generated by decades of pharmacognosy supported by the rapid evolution of chemical, biological and computational techniques. How can we cope with this overwhelming mass of information? Reverse pharmacognosy was introduced with this aim in view. It proceeds from natural molecules to organisms that contain them via biological assays in order to identify an activity. In silico techniques and particularly inverse screening are key technologies to achieve this goal efficiently. Reverse pharmacognosy allows us to identify which molecule(s) from an organism is (are) responsible for the biological activity and the biological pathway(s) involved. An exciting outcome of this approach is that it not only provides evidence of the therapeutic properties of plants used in traditional medicine for instance, but may also position other plants containing the same active compounds for the same usage, thus increasing the curative arsenal e.g. development of new botanicals. This is particularly interesting in countries where western medicines are still not affordable. At the molecular level, in organisms, families of metabolites are synthesized and seldom have a single structure. Hence, when a natural compound has an interesting activity, it may be desirable to check whether there are more active and/or less toxic derivatives in organisms containing the hit-this corresponds to a kind of “natural combinatorial” chemistry. At a time when the pharmaceutical industry is lacking drug candidates in clinical trials, drug repositioning -i.e. exploiting existing knowledge for innovation-has never been so critical. Reverse pharmacognosy can contribute to addressing certain issues in current drug discovery- such as the lack of clinical candidates, toxicity - by exploiting existing data from pharmacognosy. This review will focus on recent advances in computer science applied to natural substance research that consolidate the new concept of reverse pharmacognosy.