Bringing multimodal experience into search journey became of high interest lately: searching images with text, or looking inside an audio file, combining that with the rgb frames of a video stream. Today, vector search algorithms (like FAISS, HNSW, BuddyPQ) and databases (Vespa, Weaviate, Milvus and others) make these experiences a reality. But what if you as a user would like to stay with the familiar Elasticsearch / OpenSearch AND leverage the vector search at scale? In this talk we will take a hardware acceleration route to build a vector search experience over products and will show how you can blend the worlds of neural search with symbolic filters. We will discuss use cases where adding multimodal and multilingual vector search will improve recall and compare results from Elasticsearch/OpenSearch with and without the vector search component using tools like Quepid. We will also investigate different fine-tuning approaches and compare their impact on different quality metrics. We will demonstrate our findings using our end-to-end search solution Muves which combines traditional symbolic search with multimodal and multilingual vector search and includes an integrated fine-tuner for easy domain adaptation of pre-trained vector models.