General-purpose multimodal embedding models like Qwen/Qwen3-VL-Embedding-2B are trained on diverse data to perform well across a wide range of languages and tasks: image-text matching, visual question answering, document understanding, and more. But this generality means the model is rarely the best choice for any specific task.

Consider Visual Document Retrieval: given a text query like "What was the company's Q3 revenue?", the model must find the most relevant document screenshot from a corpus of thousands. This requires understanding document layouts, charts, tables, and text, which is a very different skill from e.g. matching pictures of shoes with product descriptions.

By finetuning on domain-specific data, the model can learn these specialized patterns. In my experiment, finetuning improved NDCG@10 from 0.888 to 0.947, ahead of every recent multimodal model I tested, including ones up to 4x larger.

Training multimodal Sentence Transformer models involves the same components as training text-only models: