This paper introduces a novel glyph-based design for music representation that leverages deep latent features to improve userdirected search for music discovery. We propose a system that combines a pre-trained neural network model for high-level music feature extraction with dimensionality-reduction methods for effective visual mapping of the intrinsic characteristics that help distinguishing a song. We provide a search-by-icon user interface (UI) that integrates glyph based on the neural features in combination with other novel navigation methods to achieve intuitive search and exploration. A detailed user study validates our approach, demonstrating its efficacy in enabling swift song clustering, identification, and retrieval. Our findings reveal that our visual representation not only speeds up the music searching process but also fosters increased user interaction with digital music libraries, representing a valuable contribution to the domain of music exploration and retrieval.