Fréchet ChemNet Distance is an evaluation metric for generative models that is particularly geared towards applications in drug design. It scores diversity and similarity in biological and chemical properties using the hidden layer of a neural network trained to predict biological activities. Products of the cut-and-join crossover tend to received rather high scores between 14.55 to 40.90 indicating the need to filter recombinants to enrich drug-like molecules. Similarly, the majority of recombinants score low in the Quantitative Estimation of Drug-likeness (QED) scale, which is based on molecular properties found in approved drugs. Nevertheless, 9% to 22% of the crossover products pass the medicinal chemistry filters (MCF) and pan assay interference compounds (PAINS) filters .