One in every 10 human proteins binds RNA. Sequence and structure of RNA determines its affinity to these post-transcriptional regulators. However, our understanding of the grammar underlying RNA-protein interactions remains incomplete. This is in large part due to the impact of RNA structure on regulatory interactions that is often ignored. For more than a decade now, we have been focused on developing strategies that enable systematic identification of structural RNA elements. Here, we will showcase the traditional approaches that are often utilized to tackle cis-regualtory element discovery in general and structured element discovery in particular. We will discuss context-free grammars as a versatile data structure that are ideally suited for modeling RNA elements, and how they can be leveraged for tackling this problem. We will also cover recent experimental and computational advances in the field that has prompted us to carve out new paths for tackling this problem.