Gravitational waves are now observed routinely. Therefore, data analysis has to keep up with ever improving detectors. One relatively new tool to search for gravitational wave signals in detector data are machine learning algorithms that utilize deep neuralnnetworks. The first successful application was able to differentiate time series strain data that contains a gravitational wave from a binary black hole merger from data that consists purely of noise. This work expands the analysis to signals from binary neutron star mergers, where a rapid detection is most valuable, as electromagnetic counterparts might otherwise be missed or not observed for long enough. We showcase many different architecture, discuss what choices improved the sensitivity of our search and introduce a new multi-rate approach. We find that the final algorithm gives state of the art performance in comparison to other search pipelines that use deep neural networks. On the other hand we also conclude that our analysis is not yet able to achieve sensitivitiesthat are on par with template based searches. We report our results at false alarm rates down to ~30 samples/month which has not been tested by other neural network algorithms. We hope to provide information about useful architectural choices and improve our algorithm in the future to achieve sensitivities and false alarm rates that rival matched filtering based approaches on.