For chemical applications, crossover products that represent chemically plausible molecules are desired. Benchmarking our procedure using a wide variety of descriptors including 3D embeddability shows that a large fraction of the offspring of real-world molecular graphs satisfies these chemical constraints. Moreover, since alternative cut-and-join crossover products can be generated very efficiently, it is feasible to filter implausible graphs and/or graphs with other undesirable properties, so that GA runs can be restricted to wellformed molecules. This view is reinforced by the successful application of cut-and-join crossover to four drug-design tasks using the REvolD software. This showcase example led to predictions of candidate ligands with predicted binding constants substantially exceeding the best-known ligands. Of course, these computational candidates need follow-up with respect to their properties as drug designs, but in any case demonstrate the efficacy of cut-and-join crossover in a CADD setting.