In silico methods based on the comparison of the 3D molecular shapes of aligned molecules are predestined for use in target prediction because of their ability to recognize similarity among structurally dissimilar compounds, as long as their molecular shapes (or at least parts of their molecular shapes) are preserved. Most shape-based methods take the distribution of chemical features ("color") into account, which contributes substantially to their performance. They form the basis of several target prediction approaches and are also attractive tools for virtual screening and scaffold hopping. Here, we systematically investigate the capacity of a leading 3D shape-based approach to identify the macromolecular targets of structurally complex small molecules (CSMs) on the basis of their molecular similarity with non-CSMs. In the context of small-molecule drug discovery, 3D shape-based screening, and this study alike, non-CSMs are compounds that medicinal chemists would identify as typical drug-like small molecules of low structural complexity. In contrast, CSMs represent less conventional compounds, characterized by higher molecular weight (larger size), larger numbers of conformational degrees of freedom and/or higher 3D shape complexity (Figure ). CSMs include complex natural products and macrocyclic compounds, many of which are of high relevance to drug discovery but typically lack experimental data. Therefore, if it is found in this study that computational approaches based on 3D shape-based alignment are indeed capable of deriving the likely macromolecular targets of CSMs based on data measured for more conventional small molecules, this could open new avenues to support drug discovery efforts in less densely populated, and hence more innovative, areas of the relevant chemical space.