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Neural transducer ASR models achieve state of the art accuracy on many tasks, however rare word recognition poses a particular challenge as models often fail to recognise words that occur rarely, or not at all, in the training data. Methods of contextual biasing, where models are dynamically adapted to bias their outputs towards a given list of relevant words and phrases, have been shown to be effective at alleviating this issue. While such methods are effective at improving rare word recognition, over-biasing can lead to degradation on common words. In this work we propose several extensions to a recently proposed trie-based method of contextual biasing. We show how performance of the method can be improved in terms of rare word recognition, especially in the case of very large catalogues, by introducing a simple normalisation term, how the method can be trained as an adapter module, and how selective biasing can be applied to practically eliminate over-biasing on common words.