In recent years RNA-seq protocols have been developed to investigate a variety of biological problems by measuring the abundance of different RNAs. Many study designs involve performing expensive preliminary studies to screen or optimize experimental conditions. Testing a large number of conditions in parallel may be more cost effective. For example, analyzing tissue/environment-specific gene expression generally implies screening a large number of cellular conditions and samples, without prior knowledge of which conditions are most informative (e.g., some cell types may not respond to certain treatments). To circumvent these challenges, we have established a new two-step high-throughput RNA-seq approach: the first step consists of gene expression screening of a large number of conditions, while the second step focuses on deep sequencing of the most relevant conditions (e.g., largest number of differentially expressed genes). This study design allows for a fast and economical screen in step one, with a more efficient allocation of resources for the deep sequencing of the most biologically relevant libraries in step two. We have applied this approach to study the response to 23 treatments in three lymphoblastoid cell lines demonstrating that it should also be useful for other high-throughput transcriptome profiling applications requiring iterative refinement or screening.