Cellular fate specifications and functions are controlled through precise and complex patterns in gene expression. Although all cells in the human body share the same DNA sequence, the expression of RNA and proteins is diverse across tissue and cell types. This orchestrated control of thousands of genes is performed by transcription factors which bind to DNA regulatory elements and modulate gene expression through assembly of transcriptional complexes or interactions with other transcriptional proteins. These regulatory elements harbor sequence variants that can impact their ability to bind transcription factors and thus influence gene expression to ultimately manifest in various traits and diseases. Our ability to interpret the functional consequences of sequence variation relies on understanding the function of the sequence the variation lies in. However, our knowledge of regulatory element function and to which genes they regulate is still limited to a small number of individually validated sequences or predicted regions from large-scale genomic dataset correlations. Recent advancements in massively parallel reporter assays have enabled the testing of millions of DNA sequences and their regulatory activity but are limited to testing outside of their native contexts. Thus, our goal was to develop scalable methods based on the CRISPR/Cas9 system to facilitate the screening of regulatory element function in their endogenous state in high throughput. We first sought to adapt CRISPR/Cas9 based epigenetic repressors and activators to perturb hundreds of putative regulatory elements in their endogenous context and screen their contribution to a single gene’s expression in single pooled experiments. Our results demonstrated the capability of the system to accurately assign function to both known and novel regulatory elements. Finally, we sought to apply the system to screen more than 100,000 putative regulatory elements for essentiality and their contribution towards cellular fitness. Overall, these studies demonstrate novel methods for decoding regulatory networks and furthering our ability to assign function to regulatory sequence influencing disease.