For example, DELFI, a recently developed recommender system for functional selection for excited state calculations of small organic molecules, required the construction of an initial dataset of 828 282 single-point TD-DFT calculations and over 21 000 reference values. An unrelated, and much less expensive, strategy to reduce the odds of suboptimal DFT functional selection consists of 'mindless benchmarking'. This approach, pioneered by Grimme and co-workers, involves random generation of artificial molecular geometries, after which a representative set is sampled with a particular focus on diversity. In this manner, the introduction of biases during the benchmarking dataset construction is limited, increasing the likelihood that the resulting set is at least somewhat representative of the broader chemical space that it aims to describe. Despite the promise of this approach for benchmarking some specific chemistry-related tasks more reliably, the strategy is not ideally suited for others. For example, when benchmarking reaction kinetics, one typically aims to identify a good functional approximation for specific reaction classes, across a well-defined scope of the accessible chemical space, so that a purely randomized approach is hard to implement.