Imagine typing in "a song similar to Superstition by Stevie Wonder" and instantly getting a playlist of perfect matches. That's the power of text-to-music retrieval (TTMR), and it's about to get a whole lot smarter. Traditional TTMR systems struggle with complex queries, focusing mainly on descriptive keywords like "genre" or "mood." But what about requests for songs *like* your favorites? Researchers are tackling this challenge with a new model called TTMR++. It uses a clever combination of a fine-tuned large language model (LLM) and rich metadata to understand the nuances of your musical desires. This LLM, trained on massive datasets of music tags and captions, generates detailed song descriptions. These descriptions, combined with metadata like artist, album, and track titles, give the model a deep understanding of the music. But the real magic happens with a knowledge graph that links similar artists together. So, if you ask for something like Stevie Wonder, the model knows to recommend artists like Herbie Hancock, based on connections made through the graph. TTMR++ is a game-changer. It's not just matching keywords; it's understanding musical relationships and delivering personalized results. This research opens exciting doors for music discovery, making it easier than ever to find the perfect soundtrack for any moment.