Natural products (NPs) are a source of inspiration for drug discovery due to their high potency and biological selectivity, which has translated in remarkable success in treating infectious diseases and cancer. However, cheminformatic modeling of NPs has been limited because of their diversity from typical drug-like molecules (on which computational pipelines are usually developed), e.g., in terms of their broader molecular weight distribution, multiple stereocenters, a higher fraction of sp -hybridized carbons and extended ring systems. This issue is further compounded by a lack of biological annotations for NPs and the widespread presence of activity cliffs due to their highly specialized biological functions. One of the key steps of cheminformatics pipelines is how to encode structural information into 'machinereadable' formats for further processing. This can be achieved through the so-called molecular descriptors, which convert selected molecular features into one or more numbers via a pre-defined algorithm. Among various descriptors applied to natural products, molecular fingerprints -which convert a molecular structure into a vector -bear promise to capture structural information on natural products (e.g., presence or absence of certain substructures). In fact, fingerprints generally provide satisfactory performance for quantitative structure-activity relationship (QSAR) modeling, even in the presence of activity cliffs. Given the relevance of fingerprints in cheminformatics, over 30 years of research in the field have led to a broad and diverse selection of fingerprinting algorithms. However, while extensive research exists on the performance of these algorithms on synthetic, drug-like molecules, little is known about the best practices for natural products encoding.