Feature descriptors in histopathological images are an important challenge for the implementation of Content-Based Image Retrieval (CBIR) systems, an essential tool to support pathologists. Deep learning models like Convolutional Neural Networks and Vision Transformers improve the extraction of these feature descriptors. These models typically generate embeddings by leveraging deeper single-scale linear layers or advanced pooling layers. However, these embeddings, by focusing on local spatial details at a single scale, miss out on the richer spatial context from earlier layers. This gap suggests the development of methods that incorporate multi-scale information to enhance the depth and utility of feature descriptors in histopathological image analysis. In this work, we propose the Local–Global Feature Fusion Embedding Model. This proposal is composed of three elements: (1) a pre-trained backbone for feature extraction from multi-scales, (2) a neck branch for local–global feature fusion, and (3) a Generalized Mean (GeM)-based pooling head for feature descriptors. Based on our experiments, the model’s neck and head were trained on ImageNet-1k and PanNuke datasets employing the Sub-center ArcFace loss and compared with the state-of-the-art Kimia Path24C dataset for histopathological image retrieval, achieving a Recall@1 of 99.40% for test patches.