The recent advancements in the field of digital library navigation and search relevance have seen significant strides, particularly in leveraging deep learning and large language models (LLMs). Innovations in computer vision, such as the use of Vision Transformers (ViT) and Contrastive Language-Image Pre-training (CLIP), have enabled more effective retrieval and classification of visual materials within digitized collections. These technologies are not only enhancing the accessibility of visual heritage but also contributing to the cleaning and organization of image datasets. Additionally, the integration of LLMs into search relevance models has shown promise in improving the accuracy and scalability of search systems, particularly in handling multilingual and long-tail queries. The distillation of LLMs' capabilities into smaller, more efficient models has further advanced the practical application of these technologies in real-world search engines. Overall, the field is moving towards more sophisticated and efficient methods for managing and retrieving information from diverse and complex digital libraries.