A recent genomics-based baraminology method has been developed that measures the gene content similarity (the Jaccard Coefficient Value, or JCV) between species and assigns them to individual baramins. The method is based on the creationist assumption that genes are conserved across genomes within a baramin and represent orthological functional units. Species from the same baramin should contain many common genes and thus have a high JCV, whereas species from different baramins should have a low JCV. This method has been further developed and estimates baramins based also on k-means clustering. The method also calculates two parameters, the pan-genome quotient (PGQ) and the completeness index (CI), both of which describe how much genome erosion via gene loss has occurred in the pan-genome of the archebaramin since the Fall. The PGQ measures the intersect/union of all genes in all species in a given baramin, while the CI measures the number of genes in all species in the baramin divided by the number of species in the baramin times the size of the union of orthologous genes. This method has been heretofore used in the analysis of Nucleocytoplasmic large DNA viruses (NCLDVs, which bear remarkable similarities to bacteria), Archaea, and insects. The method is applied to a data set of 26 fungal species in the present paper. The algorithm predicted three putative baramins, with seven species from Pezizomycotina, three from Agaro/Ustilagomycotina, and 15 from Saccharomycotina. Based on previous experience, there is no single JCV cutoff by which species can be assigned into the same or different baramins. For example, bacterial baramins may have a rather low mean JCV due to horizontal gene transfer (HGT). In general, gene content baraminology studies depend on the biology of the organisms under study. With more and more protein data becoming available, the JCV method appears to be a promising tool for many future baraminology studies.