Therefore, despite significant efforts to utilize QM descriptors in GNNs to enhance chemical property predictions, it remains unclear which descriptors are best suited for specific tasks. This uncertainty often leads to a trial-and-error approach in descriptor selection and to varied methods for incorporating these descriptors. Moreover, the effectiveness of using QM descriptors, particularly in relation to training data size and model chemistry, remains underexplored. To address these gaps, this work aims to systematically analyze the conditions under which the use of QM descriptors is beneficial, focusing on identifying the best strategies for incorporating QM descriptors for specific chemical property prediction tasks.