As current antibiotics therapy is increasingly challenged by emerging drug resistant bacteria, new technologies are required to identify and develop novel classes of antibiotics. A major bottleneck in today’s discovery efforts, however, is a lack of an efficient and standardized method for assaying a drug candidates’ efficacy. We propose a new high-content screening approach for identifying efficacious molecules suitable for antibiotics development. Key to our approach is a new microarray-based efficacy biomarker discovery strategy. We first produced a large data set of transcriptional responses of Bacillus subtilis to numerous structurally diverse antibacterial drugs. Secondly, we evaluated different protocols to optimize drug concentration- and exposure-time selection for profiling compounds of unknown mechanism. Finally, we identified a surprisingly low number of gene transcripts (~130) being sufficient for identifying the mechanism of novel substances with reasonable accuracy (~90%). We show that the statistics-based approach reveals a physiologically meaningful set of biomarkers that can be related to major bacterial defense mechanisms against antibiotics. We provide statistical evidence that a parallel measurement of the biomarkers‘ expression guarantees optimal performance when using expression systems for screening libraries of novel substances. The general approach is also applicable to drug discovery for medical indications other than infectious diseases.