This paper aims to conduct a comprehensive study on facial-sketch synthesis (FSS). However, due to the high cost of ob taining hand-drawn sketch datasets, there is a lack of a complete benchmark for assessing the development of FSS algorithms over the last decade. We first introduce a high-quality dataset for FSS, named FS2K, which consists of 2104 image-sketch pairs spanning three types of sketch styles, image backgrounds, lighting conditions, skin colors, and facial attributes. FS2K differs from previous FSS data sets in difficulty, diversity, and scalability and should thus facilitate the progress of FSS research. Second, we present the largest-scale FSS investigation by reviewing 89 classic methods, including 25 handcrafted feature-based facial-sketch synthesis approaches, 29 gener al translation methods, and 35 image-to-sketch approaches. In addition, we elaborate comprehensive experiments on the existing 19 cut ting-edge models. Third, we present a simple baseline for FSS, named FSGAN. With only two straightforward components, i.e., facial aware masking and style-vector expansion, our FSGAN surpasses the performance of all previous state-of-the-art models on the pro posed FS2K dataset by a large margin. Finally, we conclude with lessons learned over the past years and point out several unsolved chal lenges. Our code is available at https://github.com/DengPingFan/FSGAN.