diff --git a/src/kbmod_wf/task_impls/kbmod_search.py b/src/kbmod_wf/task_impls/kbmod_search.py
index c2552e19..ca71227a 100644
--- a/src/kbmod_wf/task_impls/kbmod_search.py
+++ b/src/kbmod_wf/task_impls/kbmod_search.py
@@ -1,8 +1,17 @@
 import kbmod
 from kbmod.work_unit import WorkUnit
 
-import os
+import numpy as np
 from logging import Logger
+import os
+
+from kbmod.analysis.plotting import plot_ic_image_bounds, plot_wcs_on_sky
+from kbmod.analysis.visualizer import Visualizer
+
+from kbmod.filters.known_object_filters import KnownObjsMatcher
+from kbmod.filters.stamp_filters import filter_stamps_by_cnn
+
+from astropy.table import Table
 
 
 def kbmod_search(
@@ -89,7 +98,72 @@ def run_search(self):
         self.logger.info("Search complete")
         self.logger.info(f"Number of results found: {len(res)}")
 
+        # Match to known objects from the results
+        skybot_table_path = self.runtime_config.get("skybot_table_path", None)
+        if skybot_table_path is None:
+            self.logger.warning("No skybot table path provided, skipping filtering by known objects.")
+        else:
+            self.logger.info(f"Filtering results by known objects using table at {skybot_table_path}")
+            skytable = Table.read(skybot_table_path)
+            self.logger.info(f"Read {skybot_table_path}. There are {len(skytable)} rows.")
+            known_objs_matcher = KnownObjsMatcher(
+                skytable,
+                np.array(wu.get_all_obstimes()),
+                matcher_name="known_matcher",
+                sep_thresh=5.0,  # Observations must be within 5 arcsecs.
+                time_thresh_s=30,  # Observations must match within 30 seconds.
+                name_col="Name",
+                ra_col=f"ra_{wu.barycentric_distance}",
+                dec_col=f"dec_{wu.barycentric_distance}",
+                mjd_col="mjd_mid",
+            )
+
+            # Carry out initial matching to known objects and populate the matches column.
+            known_objs_matcher.match(res, wu.wcs)
+
+            # Filter the matches down to results with at least 10 observations.
+            min_obs = 5
+            known_objs_matcher.match_on_min_obs(res, min_obs)
+
+        # Filter results by CNN
+        ml_model_path = self.runtime_config.get("ml_model_path", None)
+        if ml_model_path is None:
+            self.logger.warning("No ML model path provided, skipping filtering by CNN.")
+        else:
+            self.logger.info(f"Filtering results by CNN using model at {ml_model_path}")
+            orig_res_len = len(res)
+            filter_stamps_by_cnn(
+                res,
+                ml_model_path,
+                coadd_type="weighted",
+            )
+            res.filter_rows(res["cnn_class"])
+            self.logger.info(
+                f"Filtered {orig_res_len - len(res)} results using CNN model at {ml_model_path}"
+            )
+
         self.logger.info(f"Writing results to output file: {self.result_filepath}")
         res.write_table(self.result_filepath)
 
+        self.logger.info(f"Writing daily_coadds to output file: {self.result_filepath}")
+        # Now add some convenience plots to the results.
+        # Plot the daily coadds for each result.
+        res.table["stamp"] = res.table[
+            "coadd_weighted"
+        ]  # We store the combined stamps in the results table.
+
+        viz = Visualizer(
+            wu.im_stack, res
+        )  # The image data are derived from the reprojected WorkUnit.
+        viz.generate_all_stamps(radius=config["stamp_radius"])
+        viz.count_num_days()  # This feature enables the daily image co-addition.
+
+        # For each result, in the results table, plot the daily coadds.
+        for res_idx in range(len(res)):
+            res_uuid = res[res_idx]["uuid"]
+            daily_coadds_filename = os.path.join(
+                self.results_directory, f"{res_uuid}_daily_coadds.png"
+            )
+            viz.plot_daily_coadds(res_idx, filename=daily_coadds_filename)
+    
         return self.result_filepath