Equality saturation has successfully been applied in many domains. Yet, scaling issues hold back its success in even more applications. The underlying e-graph data structure can grow rapidly quickly consuming all available resources.

Guided Equality Saturation proposed a solution by breaking challenging rewrite problems into a sequence of equality saturations. This enables the technique to scale further and solve complex rewrite problems far out of reach of standard equality saturation. However, this technique relies on the human experts to provide insights in the form of \emph{guides} that describe when to stop one equality saturation and start the next.

In this talk, we are going to present our ongoing efforts to reduce the reliance on human experts. In our \emph{Machine Learning Guided Equality Saturation}, the ambition is to automatically generate guides using a machine learning model to enable the scaling of Equality Saturation to more complex applications. We report on the current state of our research and the machine learning model we are developing.