Historically, SA scores have been employed as proxies for compound accessibility assuming that synthesizability implies availability and ignoring other relevant aspects such as compound cost. In practice, SA scores have been used to classify compounds as synthesizable nonsynthesizable selecting different thresholds. We sought to test how this strategy performs on two sets of molecules, a dataset of purchasable compounds (PC) and a dataset of non-purchasable natural products (NPNP) using four different types of SA scores: SAscore 10 , SCScore 11 , SYBA , and RAscore 8 (Figure , a-d). As a first approximation all the PC molecules should be classified as synthetically feasible, and most of them as highly accessible, whereas most of the NPNP compounds should be considered hard to synthesize. As Figure a-e shows, none of the methods, including CoPriNet, which will be discussed later, perfectly separate the two compound sets. However, this is not surprising nor potentially even desired since not all NPNP are synthetically infeasible, nor are all purchasable compounds easy to make. In order to get a better estimation of the actual number of synthetically feasible NPNP compounds we computed ManifoldSA, a pure retrosynthesis-based score (see Methods for more details). Retrosynthesis-based approaches tend to be far more accurate than standard fast SA scores and as such, we can consider them as an (imperfect) ground truth when evaluating simple SA scores. For the NPNP, ManifoldSA estimates that ~24% of the compounds are synthesizable, with 4.6% of those being easily synthesizable (see Figure ). Whereas for the PC dataset, ~6% of the compounds were regarded as infeasible despite being commercially available. While these numbers also suggest that retrosynthesis predictions are not perfect, they are more accurate than fast SA scores. So, it is interesting to consider them as ground truth (Figure f-j). The results obtained in this case, although worse for all methods, are similar to the ones of the previous experiment.