Our initial experiments on internal binaries demonstrate that combining existing MLGO features with IR2Vec embeddings yields additional code size reductions of up to 5% in comparison to `-Os` and 4% in comparison to `-Os` with MLGO Inliner. This talk will outline the design of IR2Vec, the plan for upstreaming its support into LLVM, and discuss experimental results validating its effectiveness and scalability on real-world datacenter binaries. Specifically, we will describe how IR2Vec embeddings are used for driving ML-Guided Compiler Optimizations, focusing on our efforts to enhance current MLGO infrastructure and its possible extensions.