The performance of programming-by-example systems varies significantly across different tasks and even across different examples in one task. The key issue is that the search space depends on the given examples in a complex way. In particular, scalable synthesizers typically rely on a combination of machine learning to prioritize search order and deduction to prune search space, making it hard to quantitatively reason about how much an example speeds up the search. We propose a novel approach for quantifying the effectiveness of an example at reducing synthesis time. Based on this technique, we devise an algorithm that actively queries the user to obtain additional examples that significantly reduce synthesis time. We evaluate our approach on 30 challenging benchmarks across two different data science domains. Even with ineffective initial user-provided examples for pruning, our approach on average achieves a 6.0X speed-up in synthesis time compared to state-of-the-art synthesizers.