Session-based recommendation targets a core scenario in e-commerce and online browsing. Given a sequence of interactions of a visitor with a selection of items, we want to recommend to the user the next item(s) of interest to interact with. This machine learning problem is crucial for e-commerce platforms, which aim to recommend interesting items to buy to users browsing the site. We created a scalable adaptation of the state-of-the-art session-based recommendation algorithm VS-kNN. Our approach minimises intermediate results, controls the memory usage and prunes the search space with early stopping. Consequently, this approach drastically outperforms VS-kNN in terms of prediction latency, while still providing the desired prediction quality advantages over neural network-based approaches. Furthermore, we designed and implemented a real-world system around this algorithm, which is deployed in production at bol.com.