Site saturation mutagenesis (SSM) is a powerful mutagenesis strategy for protein engineering and directed evolution experiments. However, limiting factors using this method are either biased representation of variants, or limiting library size. To overcome these hurdles, we generated large scale targeted synthetic SSM libraries using massively parallel oligonucleotide synthesis and benchmarked this against an error-prone (epPCR) library. The yeast glucose activated GPCR-Gpr1 was chosen as a prototype to evolve novel glucose sensors. We demonstrate superior variant representation and several unique hits in the synthetic library compared to the PCR generated library. Application of this mutational approach further builds the possibilities of synthetic biology in tuning of a response to known ligands and in generating biosensors for novel ligands.