In the past year the interest in Neural Search and vector search engines increased heavily. They promise to solve multi modal, cross modal and semantic search problems with ease. However, when quickly trying Neural search with off-the-shelf pre-trained models the results are quite disillusioning. They lacking knowledge about the data at hand. In order to explicitly solve model finetuning for search problems we implemented an open-source finetuner. It is directly usable with several vector databases due to the underlying data structure. In our talk we present our methodology and performance on an example dataset. Afterwards, we show how well the approach transfers to other datasets, such as deepfashion, geolocation geoguessr and more. It will give hands-on guidance on how you can finetune a model in order to make your data better searchable.