The present computational search was limited to docking candidates predicting the highest affinities to cavity interfaces of Vif blue Vif red sA3G±RNA (Graphical Abstract). Explorations were limited to highest docking-affinities to reduce possible in vivo active concentrations and side-effects. To produce new molecules predicting high docking-affinities, tens of thousands of raw-children were randomly generated from selected parents and thousands selected for best Vif blue Vif red sA3G±RNA cavity-fitting (fitted-children). For such explorations the DataWarrior Build Evolutionary Library (DWBEL) java-based co-evolution algorithms were employed as alternative to screening extralarge drug-like molecular banks or predictive learning docking models from protein sequences . Similar co-evolutions had predicted ligand-affinity improvements when targeting different protein-ligand pairs. For instance, new antibiotics for resistant Staphilococcus 42 , Abaucin-derivatives against Acinetobacter 43 , nonhuman anticoagulant rodenticides , monkeypox Tecovirimat-resistant mutants , inner-cavity SARS omicron , inflammatory coronavirus ORF8 protein , prokaryotic models of human K + channels 48 , inner-cavity of VHSV rhabdovirus , malaria circumsporozoite protein or RSV resistant mutations .