Accepting that the 𝑦-axis spans 45 orders of magnitude, and given the known intricacies associated with nucleation rate calculations , there appears to be reasonable consensus in the rates from unbiased and biased simulation strategies. System size effects (including the large critical nucleus sizes that are observed at low supersaturation, as well as solute depletion) mean that simulation studies typically adopt high values of 𝑆. In experiments, however, NaCl nucleation becomes so fast at the higher end 𝑆 that special experimental techniques must be used to determine rates even up to 𝑆 ≈ 2. In the small range of 𝑆 where simulations overlap with experiments, a comparison of the rates leaves much to be desired. While it is clear from Zimmerman et al. that the vast difference in the rates from seeding simulations depends on the definition of the order parameter (OP), this issue is addressed by the mislabelling analysis of Lamas et al. . Taking the latter seeding results along with the forward flux sampling (FFS) rates from Jiang et al. , the force field predicts crystal nucleation rates that are approximately 20 orders of magnitude lower than experiments. This discrepancy may indicate inaccuracies in the molecular model; however, we note that there is even a discrepancy in the experimental data, spanning around five orders of magnitude. This example, simple system typifies the challenge of matching simulation and experimental studies of crystal nucleation. The moonshot challenge, therefore, is to develop simulation and experimental strategies that consistently reproduce rates that can be confidently compared to one another. Mechanistically, the outlook is less bleak, as both simulations and experiments indicate one-and two-step crystal nucleation according to the solution supersaturation. Impressive advances in simulation capabilities were made over the previous decade; however, it is undoubtedly evident to the reader that the studies we highlight where quantitative thermodynamics and kinetics have been evaluated apply to very simple systems. Even so, the determination of nucleation rates requires exhaustive computational resources. Sampling crystal nucleation pathways in more complex systems, such as in solutions of molecules with conformational freedom that are so important to the pharmaceutical industry, is still a major challenge that is yet to be realised beyond small molecules. Nevertheless, increasing computational power and the progress made in machine learning techniques may make the routine simulation of crystal nucleation realisable in the near future.