Administering growth promoters to meat-producing animals is strictly regulated within the European Community to preserve consumers' safety. Nevertheless, illicit misuse of steroids and other veterinary drugs to increase animal production has still been found by National Residues Control Plans (NRCPs), mainly based on expensive targeted multi-class/multi-residue analytical methods, used for confirming quantification analysis of these residues in tissues and biological fluids. The setup of complementary novel diagnostic methods, based on indirect biomarkers of exposure to anabolic substances is, therefore, needed to update current tests available in NRCPs at the screening level, where cheaper, sensitive and untargeted methods are required. Biological effects of illicit treatment transcriptomic analysis has reached a good grade of standardization, identified by microarray and RNAseq technologies of large panels of differentially expressed gene targets (DEG) related to abuse of different classes of anabolic molecules (e.g. sex steroids, thyreostats, beta 2-agonists, glucocorticoids, etc.). The aim of the work was to identify a minimum set of molecular markers able to detect illicit administration of two of the most recurrent classes of growth promoters (sex steroids and B2-agonists) on formalin-fixed paraffin-embedded (FFPE) tissues, being a potentially straightforward sampling strategy at the slaughterhouse level. A PCR array to allow quantitative profiling of 48 genetic targets was, therefore, developed on FFPE liver samples from veal calves. Conventional univariate and alternative approaches based on multivariate classification methods were considered for gene expression profiling of 92 samples collected from different animal trials, where effects of nandrolone, estradiol, their combination, and clenbuterol were studied. The developed Partial Least Squares-Discriminant Analysis (PLS-DA) models show the successful setup of a novel screening strategy based on multiple transcriptional biomarkers, able to expand tested sample sizes, and consequently strengthen current NRCPs.