To address those requirements, this work computationally explored a wider chemical/chemotype space by generating novel libraries designed-ondemand by random evolution of one selected parent corresponding to one previously identified star-shaped molecule binding the inner-cavity of first SARS-Cov-2 isolates obtained with the seeSAR (sS) program algorithm . The children molecules were generated by extending the number of new star-shaped molecules while still fitting the inner-cavity of the Omicron variants with one DataWarrior (DW) algorithm. Each of the evolutionary library run, selected thousands of new children possibilities from the parent molecule, discarding tens of thousands of other generated children that fail to fit into the inner-cavity and/or to other multiple molecular criteria. The DW selected children were filtered for absence of known toxicities and two of their top-leads re-used as new parents. Once the re-evolved non-toxic children libraries were generated, consensus docking-scores were obtained by comparing DW results with those obtained from the AutoDockVina (ADV) algorithm after being normalized for excessive molecular weight and hydrophobicity with Ligand Efficiency Lipophilic Prize (LELP) metric. VENN diagrams among DW, ADV and LELP ranks were used to compare and select top-leads.