The flexibility of Knowledge Graphs to represent heterogeneous entities and relations of many types is challenging for conventional data integration frameworks. In order to address this challenge the use of Knowledge Graph Embeddings (KGEs) to encode entities from different data sources into a common lower-dimensional embedding space has been a highly active research field. It was recently discovered however that KGEs suffer from the so-called hubness phenomenon. If a dataset suffers from hubness some entities become hubs, that dominate the nearest neighbor search results of the other entities. Since nearest neighbor search is an integral step in the entity alignment procedure when using KGEs, hubness is detrimental to the alignment quality. We investigate a variety of hubness reduction techniques and (approximate) nearest neighbor libraries to show we can perform hubness-reduced nearest neighbor search at practically no cost w.r.t speed, while reaping a significant improvement in quality. We ensure the statistical significance of our results with a Bayesian analysis. For practical use and future research we provide the open-source python library kiez at https://github.com/dobraczka/kiez.