Approximate sentence matching (ASM) is an important technique for tasks in machine translation (MT) such as example-based MT (EBMT) which influences the translation time and the quality of translation output. We investigate different approaches to find similar sentences in an example base and evaluate their efficiency (runtime), effectiveness, and the resulting quality of translation output. A comparison of approaches demonstrates that i) a sequential computation of the edit distance between an input sentence and all sentences in the example base is not feasible, even when efficient algorithms to compute the edit distance are employed; ii) in-memory data structures such as tries and ternary search trees are more efficient in terms of runtime, but are not scalable for large example bases; iii) standard IR models which only cover material similarity (e.g. term overlap), do not perform well in finding the approximate matches, due to their lack of handling word order and word positions. We propose a new retrieval model derived from language modelling (LM), named LM-ASM, to include positional and ordinal similarities in the matching process, in addition to material similarity. Our IR based retrieval experiments involve reranking the top-ranked documents based on their true edit distance score. Experimental results show that i) IR based approaches result in about 100 times faster translation; ii) LM-ASM approximates edit distance better than standard LM by about 10%; and iii) surprisingly, LM-ASM even improves MT quality by 1:52% in comparison to sequential edit distance computation.