Approximate Nearest Neighbor Search (ANNS) is a fundamental algorithmic problem, with numerous applications in many areas of computer science. Locality-sensitive hashing (LSH) is one of the most popular solution approaches for ANNS. A common shortcoming of many LSH schemes is that since they probe only a single bucket in a hash table, they need to use a large number of hash tables to achieve a high query accuracy. For ANNS-(L_2), a multi-probe scheme was proposed to overcome this drawback by strategically probing multiple buckets in a hash table. In this work, we propose MP-RW-LSH, the first and so far only multi-probe LSH solution to ANNS in (L_1) distance. Another contribution of this work is to explain why a state-of-the-art ANNS-(L_1) solution called Cauchy projection LSH (CP-LSH) is fundamentally not suitable for multi-probe extension. We show that MP-RW-LSH uses 15 to 53 times fewer hash tables than CP-LSH for achieving similar query accuracies.