In light of these demands, we recommend a comprehensive decision process to achieve optimal use of QM descriptors. More specifically, given the usually high computational cost associated with generating descriptors through quantum chemistry calculations, and the significant advantage of a larger training set, we recommend prioritizing the acquisition of new training data whenever possible. However, if obtaining additional training data is not feasible or comparatively more expensive, employing QM descriptors can be a suitable alternative. To reduce computational costs, ML-estimated QM descriptors can sometimes be a practical substitute for direct QM calculations, but they may compromise the accuracy of model predictions due to inherent estimation errors. Importantly, the accuracy of QM descriptors is often crucial to model success, as even a modest level of uncertainty in the descriptors can potentially significantly reduce their effectiveness in improving model performance. Moreover, simply using a large number of QM descriptors without careful analysis is not a reliable strategy, as irrelevant descriptors can introduce noise and confuse the model, particularly when training data are scarce. While this study extensively evaluates a set of general density functional theory (DFT) QM descriptors that can be readily computed, there remains untapped potential in unexplored descriptors that could significantly benefit specific predictive tasks. However, the use of domain-specific descriptors often requires specialized expertise, which may affect model accessibility and usability. The interplay of numerous factors underscores the significance of a well-conceived descriptor selection strategy that carefully balances model reliability, robustness, explainability, and computational efficiency. To help the reader navigate these considerations more effectively, we propose a comprehensive decision process for the optimal use of QM descriptors, which is summarized in a user-friendly decision flowchart provided in the manuscript. Looking forward, future research directions include a deeper exploration of the diverse range of QM and non-QM descriptors not covered in this study, particularly those that may offer novel insights into molecular property and chemical reactivity prediction. The choice of computational methods and the impact of molecular conformers on the effectiveness of descriptors also warrant further examination. Additionally, while this work focuses on D-MPNNs, the insights and guidelines provided are likely applicable to other machine learning frameworks for chemistry.