Person　search,　consisting　of　jointly　or　separately　trained　person　detection　stage　and　person　Re-ID　stage,　suffers　from　significant　challenges　such　as　inefficiency　and　difficulty　in　acquiring　discriminative　features.　However,　certain　work　has　either　turned　to　the　end-to-end　framework　whose　performance　is　limited　by　task　conflicts　or　has　consistently　attempted　to　obtain　more　accurate　bounding　boxes　(Bboxes).　Few　studies　have　focused　on　the　impact　of　sample-specificity　in　person　search　datasets　for　training　a　fine-grain　Re-ID　model,　and　few　have　considered　obtaining　discriminative　Re-ID　features　from　Bboxes　in　a　more　efficient　way.　In　this　paper,　a　novel　sample-enhanced　and　instance-sensitive　(SEIE)　framework　is　designed　to　boost　performance.　By　analyzing　the　structure　of　person　search　framework,　our　method　refines　the　two　stages　separately.　For　the　detection　stage,　we　re-design　the　usage　of　Bbox　and　a　sample　enhancement　combination　is　proposed　to　further　enhance　the　quality　and　quantity　of　Bboxes.　SEC　can　suppress　false　positive　detection　results　and　randomly　generate　high-quality　positive　samples.　For　the　Re-ID　stage,　we　contribute　an　instance　similarity　loss　to　exploit　the　similarity　between　classless　instances,　and　an　Omni-scale　Re-ID　backbone　is　employed　to　learn　more　discriminative　features.　We　obtain　a　more　efficient　and　discriminative　person　search　framework　by　concatenating　the　two　stages.　Extensive　experiments　demonstrate　that　our　method　achieves　state-of-the-art　performance　with　a　high　speed,　and　significantly　outperforms　other　existing　methods.