In Statistical Machine Translation, some complex features are still difficult to integrate during decoding and usually used through the reranking of the k-best hypotheses produced by the decoder. We propose a translation table partitioning method that exploits the result of this reranking to iteratively guide the decoder in order to produce a new k-best list more relevant to some complex features. We report experiments on two translation domains and two translations directions which yield improvements of up to 1.4 BLEU over the reranking baseline using the same set of complex features. On a practical viewpoint, our approach allows SMT system developers to easily integrate complex features into decoding rather than being limited to their use in one-time k-best list reranking.