Under real-life conditions, an in silico method capable of ranking the targets of CSMs among the top-20 out of more than 3600 (covered) targets may well be a valuable tool for prioritizing research efforts in early drug discovery because (i) it is likely that researchers will be able to rule out many of the proteins wrongly predicted as targets based on their expert knowledge on a compound of interest and (ii) in many cases, clusters of related compounds rather than a singleton will be explored, which allows predictions for multiple queries to be combined in order to improve the signal-to-noise ratio of the target prediction approach. Given these usage scenarios, we have shown that chances are good for molecular shape-based methods to identify the macromolecular targets of small molecules, even if they are of high molecular complexity and only distantly related to any known bioactive compounds. More specifically, we found that ROCS, in combination with the TanimotoCombo score, ranked the targets of 29% of the 350 investigated CSM queries among the top-5 ranks of hit lists of more than 3600 proteins (40% if the top-20 ranks are considered). For 29 of the 35 proteins (83%), the target of interest was ranked at the top position with at least one of the ten queries. The robustness of ROCS in terms of recognizing distant similarity among pairs of molecules is impressive, and a further important advantage of this particular screening engine is that it produces alignments that are easily interpretable thanks to the lock-in effect caused by the use of hard Gaussians for chemical features. This enables chemists to quickly judge the plausibility and reliability of predictions.