The intestinal microbiota has many beneficial roles for its host. However, the precise mechanisms developed by the microbiota to influence the host intestinal cell responses are only partially known. The complexity of the ecosystem and our inability to culture most of these micro-organisms have led to the development of molecular approaches such as functional metagenomics, i.e. the heterologous expression of a metagenome in order to identify functions. This elegant strategy coupled to high throughput screening allowed to identify novel enzymes from different ecosystems where culture methods have not yet been adapted to isolate the candidate microorganisms. We have proposed to use this functional metagenomic approach in order to model the microbiota's interaction with the host by combining this heterologous expression with intestinal reporter cell lines. The addition of the cellular component to this functional metagenomic approach introduced a second important source of variability resulting in a novel challenge for high throughput screening. First attempts of high throughput screening with various reporter cell-lines showed a high distribution of the response and consequent difficulties to reproduce the response, impairing an easy and clear identification of confirmed hits. In this study, we developed a robust and reproducible methodology to combine these two biological systems for high throughput application. We optimized experimental setups and completed them by appropriate statistical analysis tools allowing the use this innovative approach in a high throughput manner and on a broad range of reporter assays. We herewith present a methodology allowing a high throughput screening combining two biological systems. Therefore ideal conditions for homogeneity, sensitivity and reproducibility of both metagenomic clones as well as reporter cell lines have been identified and validated. We believe that this innovative method will allow the identification of new bioactive microbial molecules and, subsequently, will promote understanding of host-microbiota interactions.