The performance of ML models for chemical property prediction depends on having expressive representations of chemical species and reactions. Traditional approaches often utilize expert-guided fixed molecular fingerprints, or Morgan fingerprints, for molecular featurization (a.k.a. encoding). However, recent trends have shifted toward automatically learned molecular representations, such as those obtained from graph neural networks (GNNs), mainly due to the enhanced scalability, flexibility, expressiveness, and generalizability offered by learned representations. Studies have shown that ML models that leverage learned molecular representations tend to outperform those that used fixed molecular fingerprints. Yet, this transition to representation learning requires larger datasets, as the relationship between the features (the inputs to the model) and targets (the desired outputs) in deep neural networks becomes less transparent and more complex. Moreover, molecular representations can also be classified by the type of features used to generate them. For example, structure-based representations often require only very basic chemical and structural information, such as atom types, bond orders, and connectivities, which makes them widely used because of their simplicity and extensibility. However, because this featurization strategy tends to predominantly depend on basic molecular information, a more extensive training set is often necessary to learn representations capable of handling complex chemical property prediction tasks effectively.