The last decade has seen a boost in the development of in silico approaches for the prediction of the macromolecular targets of small molecules. Progress has been fueled by, among other factors, (i) the increasing amount of chemical and biological data available in the public domain, (ii) the strategic shift from the "one drug-one target" paradigm that had dominated smallmolecule drug discovery for decades to the concept of polypharmacology, and (iii) advances in computational power and algorithms. Despite the rapid development however, it is challenging to obtain a realistic understanding of the performance of target prediction methods. In silico approaches for target prediction can be classified into similarity-based, network-based, machine learning and docking methods. A large proportion of models reported in the scientific literature are available as free public web services or commercial tools. Most models utilize information from the largest public resources of chemical and biological data, PubChem and the ChEMBL database. PubChem currently contains more than 102 million compounds and 268 million bioactivity data points, and the latest release of the ChEMBL database contains close to 2 million compounds, with more than 15.5 million measured activities. With the increasing coverage and reliability of the models, researchers have started to develop strategies for predicting the likely targets of more challenging compounds such as natural products, for which there is a notorious lack of available measured data, and macrocyclic compounds, characterized by a large number of conformational degrees of freedom in combination with distinct torsional angle preferences. For example, Reker et al. dissected the macrocyclic anti-tumor agent archazolid A and used pharmacophoric descriptions of these fragments to relate them to small molecules with known bioactivities. Several then unknown targets of archazolid A that were predicted by this approach have subsequently been confirmed in biological tests. More recently, Cockroft et al. have reported on the development of a stacked ensemble approach which, despite being trained on data for synthetic compounds, is able to predict the macromolecular targets of natural products with good accuracy.