Representations of molecules with calculated features range from computationally simple to highly complex. In general, feature extraction from a molecule can be accomplished by considering, in order of increasing computational complexity: (1) only the atoms and bonds encoded in the molecular graph, (2) the three-dimensional (3D) shape, and (3) the full electronic structure of the molecule. Molecular graph-based feature extraction methods, such as topological fingerprinting, are fast to calculate but lack 3D information that is critical for certain optimization problems. Indeed, the low-energy conformers of a molecule play an essential role in determining its chemical properties. Consequentially, 3D fingerprinting methods have been developed and recent interest in incorporating 3D information into molecular graph objects has led to a variety of feature extraction methods employing graph neural networks. More challenges in representation arise when considering conformational flexibility, solvation, non-covalent interaction, and other molecular features that can only be described by explicit 3D molecular encoding.