Although the results from models trained on subsets of descriptors seem to suggest that using a comprehensive set of descriptors generally performs comparably to models trained on the optimal subset, this parity is contingent upon the inclusion of the optimal subset within the full set. Crucially, selecting an inappropriate subset of QM descriptors can significantly impair model performance, emphasizing the importance of a detailed analysis to identify the most effective descriptors. Given these insights, we recommend a strategic approach to descriptor selection, starting with a broad set for initial rapid testing and then refining the set through systematic evaluation. Adopting this strategy can potentially not only enhance model performance by minimizing errors due to irrelevant features but also help elucidate the underlying reasons contributing to the effectiveness of specific descriptors on particular targets. Moreover, a refined descriptor set can streamline model application and future investigations by lowering the computational demands of descriptor calculation for new molecular inquiries.