Unlike the large datasets used for ML in other disciplines (e.g., image recognition), synthetic chemistry datasets are often extremely small and, to compensate, researchers often develop bespoke descriptors, which are based on expert knowledge such as mechanistic understanding or quantum chemical calculations. However, it is possible that the descriptors generated contain little relevant information and are simply perceived as distracting noise by the ML model. In this publication, we aim to investigate the relationship between descriptor complexity and ML model performance when predicting the yield of chemical reactions. Furthermore, we aim to explore how the descriptor complexity impacts closed-loop optimization, a strategy that may help to guide synthetic chemists towards optimal reaction conditions. Whilst it can be challenging for humans to identify complex relationships in large datasets, ML relies on building statistical models that adjust to the given input data and has recently proven to be a powerful tool for the successful identification of nuanced patterns in complex data. Trained ML models can be used to make predictions given inputs that lie within the defined parameters of the training dataset, referred to as interpolative tasks. This includes new combinations of already known components, such as catalysts/ligands/additives. In contrast, extrapolative tasks, which are represented by predictions of inputs which are not represented in the training data are challenging, with the predictive power of a ML model decreasing as structural differences between the training and test data increase. These extrapolative tasks require the ML model to learn about the fundamental chemical properties and, as such, the inputs are generally molecular descriptors that are based on fundamental molecular properties such as atomic distances, orbital energies or charge distributions. The application of ML as a decision-making tool during reaction optimization represents an effective combination as it accelerates experimental workflows and allows for rapid gains in understanding. Active ML-driven closed-loop optimization uses an initial dataset to make predictions about yet unseen conditions and these predictions can inform decisions about the subsequent experiments. For example, the experiments predicted to deliver the highest yields or the greatest improvement in model performance can be prioritized and conducted.