To explore a possible expansion of abaucin-derived chemotypes predicting nanomolar activities, we choose the "build evolutionary library" generation algorithm described at the DataWarrior (DW) program. This algorithm has been similarly applied on our predictions targeting other protein-ligand pair examples . This generation/selection algorithm offers a unique alternative to the screening of large chemical data banks or to the deep-learning discovery methods. The here called evolutionary docking library augmented the repertoire of abaucin-derived children by generating ~ 45000 new molecules by small chemical random changes. Only those children predicting improved affinity to the abaucin A. baumannii LolCE docking-cavity were selected for iterative generations. Thousands of abaucin-children with improved fitting to the abaucin docking-cavity could be generated by this method. Additionally, since E.coli is the most important commensal specie, DW docking to the crystallographic E.coli LolCDE target was employed to eliminate those A. baumannii abaucin-children with E.coli undesirable high affinities. The abaucin-children candidates predicting DW high affinities (low docking-scores) to A. baumannii LolCDE by both DW and AutoDockVina (ADV) were consensed to increase the accuracy of their predictions. While these results are uncertain due to be based only on a computationally predicted model and ligand putative candidates, the proposed list displayed numerous alternatives with higher affinities than abaucin.